§ INTRODUCTION

With artificial intelligence (AI) becoming increasingly integrated into people's daily lives, the concept of trust recently got a lot of traction. Trust is not only an essential element in human-AI interactions as it shapes how people use and rely on AI hoff2015trust, lee2004trust, but also a key motivation for research into explainable AI (XAI) to create more transparent AI systems lipton2018mythos. Consequently, there is a growing need for a comprehensive understanding and appropriate measurement of human trust in AI. However, the operationalization and measurement of trust are complicated by various challenges.

For one thing, a multitude of different definitions and conceptualizations of trust exist benk2022value, vereschak2021evaluate, ueno2022scoping,muir1994trust that are often not clearly distinguished from related terms (e.g., "reliance" poursabzi2018manipulating, "situational trust" hoff2015trust, "perceived trustworthiness" weitz2021let, "calibrated trust" langer2021we or "warranted trust" jacovi2021formalizing,hoffman2009dynamics). Not clearly distinguishing between these terms can lead to theoretical entanglements and divergent operationalizations of trust kohn2021measurement.
For example, trust, viewed as an attitude lee2004trust, is a subjective psychological construct, typically measured via questionnaires, also called survey scales scharowski2022trust. Meanwhile, reliance, as a behavior lee2004trust, can be assessed using more objective observational methods such as analyzing changes in an individual's behavior after being presented with an AI recommendation (e.g., switch ratios Lu2021, yin2019understanding).
Conceptualizations such as "calibrated" or "warranted" trust also require this differentiation and emphasize that the motivation of XAI should not be merely to increase trust arbitrarily and unjustifiably. Instead, trust should be aligned and calibrated to the AI's trustworthiness lee2004trust, wischnewski2023measuring. In this regard, trust is warranted when the AI is trustworthy and unwarranted when it is untrustworthy jacovi2021formalizing. Although the importance of calibrated trust has been recognized by the community wischnewski2023measuring, the corresponding perspective - that distrust in untrustworthy AI is also warranted – remains relatively underemphasized, despite being an integral factor motivating XAI jacovi2021formalizing. Indeed, distrust seems a comparatively overlooked construct in current human-AI research ueno2022scoping, scharowski2023distrust.

Beyond these theoretical challenges, empirical studies measuring trust often use single items [e.g.,][]yu2017user or develop their own questionnaires [e.g.,][]yin2019understanding, merritt2011affective. However, self-developed questionnaires and single items usually lack a rigid construction and quality assurance process and are often only used in an individual study, complicating comparing different study results furr2011scale. Thus, it has been recommended to use validated trust questionnaires wischnewski2023measuring whose psychometric quality (i.e., objectivity, reliability, and validity) has been scrutinized.
But even if researchers address these challenges and use standardized questionnaires for measuring trust, they have to resort to and adapt scales from other disciplines, as there is no validated questionnaire for trust in AI.
For example, it is common practice among researchers to use the Trust between People and Automation scale (TPA) by jian2000foundations and rephrase the questionnaires' items to fit the study context vereschak2021evaluate. However, such practices raise concerns about whether the modified scale still measures what it was initially intended to measure furr2011scale, juniper2009modified.
More recently, hoffman2023measures recommended a Trust Scale for the AI Context (TAI) that is based on existing trust scales, including the TPA. While adopting items from other scales is a common first step in questionnaire development, a scale's psychometric quality should be reevaluated after each modification or adoption furr2011scale,juniper2009modified. However, this has yet to be the case for the TAI. In fact, most studies measuring human trust in AI do not report the psychometric quality of the questionnaires they used vereschak2021evaluate and only recently, Lai_2023 pointed out that the research community lacks practices to validate and reuse standardized measurements. At best, this makes it challenging for other researchers to replicate or build upon existing work. At worst, using non-validated trust questionnaires in the context of AI can generate research results that do not withstand psychometric scrutiny and thus lead to ambiguous or inconsistent findings, impeding progress in human-AI interaction and XAI research.
Despite this need for standardized measures, the psychometric quality of both the TPA and TAI remains to be thoroughly investigated in an AI context. Our research aims to fill this research gap by validating the TPA and TAI in a pre-registered online experiment, following current best practices for investigating questionnaire quality.

The contribution of this article is threefold. First, we present the first comprehensive psychometric evaluation of jian2000foundations's TPA scale in an AI setting. Second, we conduct an extensive independent psychometric evaluation of the TAI by hoffman2023measures. Third, we compare the two trust scales and offer recommendations and guidance for researchers and practitioners who want to use the TPA and TAI in the context of AI.
Results from the online experiment ($N = 1485$) show that the TAI performs well psychometrically.
Concerning the TPA, somewhat acceptable quality was only achieved after removing items and when considering a two-factor model (trust and distrust) instead of the initially proposed single-factor model.
Based on these findings, we advocate for future work on the TPA or the development of a new scale explicitly designed for the context of AI which accounts for the distinction between trust and distrust.
Other disciplines have long been in a critical discourse on whether trust and distrust constitute the same construct at opposite ends of a continuum or should be treated as separate constructs on two distinct dimensions.
However, this discourse has yet to find any real resonance in the XAI community, which could be an underappreciated opportunity for a more inclusive understanding of trust and distrust. Such a distinction could account for both warranted trust for trustworthy AI and warranted distrust for untrustworthy AI, which aligns more closely with the objectives of XAI jacovi2021formalizing. Ultimately, our work provides future research with more reliable and valid tools for measuring trust in AI and extends the current understanding of trust for a more comprehensive and holistic understanding of human trust and distrust in human-AI research.

§ RELATED WORK

§.§ Defining Trust in AI

Trust has been studied extensively across various disciplines for decades, including philosophy fukuyama1996trust, social sciences gambetta2000can, and economics berg1995trust.
This comprehensive exploration has contributed to a multifaceted perspective on trust and, at times, divergent conceptualizations across different academic domains. For instance, within the realm of social sciences, trust has been defined as the anticipation of non-hostile behavior; in economic frameworks, trust is often conceptualized through game theory; and within philosophy, it is anchored in moral relationships among individuals andras2018trusting. Researchers have introduced accounts of interpersonal trust mayer1995integrative that apply to the human-machine interaction lee2004trust and which more recently have been extended to trust in the human-AI interaction jacovi2021formalizing.

There are several definitions benk2022value, vereschak2021evaluate, ueno2022scoping and models [e.g.,][]mayer1995integrative, lee2004trust, TAM, hoff2015trust, mcknight2001trust, Liao2022, toreini2020relationship of trust in AI in circulation. However, the most commonly used definition in the human-AI trust literature vereschak2021evaluate, ueno2022scoping is attributed to 's definition of trust in automation as "the attitude that an agent will help achieve an individual's goals in a situation characterized by uncertainty and vulnerability" [p. 6] lee2004trust. This emphasis on uncertainty and vulnerability is consistent with the influential rousseau1998not and also widely adopted vereschak2021evaluate definition of trust by mayer1995integrative, which lee2004trust's work is based on. Indeed, most definitions define trust either explicitly or implicitly as an attitude vereschak2021evaluate, castelfranchi2010trust and necessitate the presence of risk, uncertainty, and vulnerability for trust to exist rousseau1998not, vereschak2021evaluate, hoff2015trust, castelfranchi2010trust, buccinca2020proxy. For this reason, we will adopt lee2004trust's definition but also draw on mayer1995integrative's corresponding model of trust for the remainder of this paper.

§.§ Forming Trust in AI

Trust does not form on its own accord but has its foundation in the attributes, characteristics, or actions of the trustee hoff2015trust, mayer1995integrative. mayer1995integrative referred to these qualities as "factors of trustworthiness" and suggested that "ability," "benevolence," and "integrity" provide the foundation for the development of trust.
It is crucial to note the distinction between the perceived trustworthiness of the trustor and the actual trustworthiness of the trustee schlicker2022calibrated. While the actual trustworthiness is a property of the trustee, the perceived trustworthiness is an assessment of these properties on the side of the trustor mayer1995integrative, schlicker2022calibrated.
For example, based on a chatbot's repeated demonstration of writing excellent poetry, an individual may conclude that the chatbot has high competence. This assessment can contribute to the individual's perception of the chatbot as trustworthy, which provides the basis for trust.

Drawing on mayer1995integrative's work, lee2004trust extended the factors contributing to trustworthiness to the context of automation and included performance (i.e., what the automation does), process (i.e., how the automation works) and purpose (i.e., why the automation was developed) as a basis of trust.
More recent research has focused on trustworthiness factors specific to AI systems toreini2020relationship, Liao2022, Thornton2021, kaplan2023trust. For example, Liao2022 introduced a trust model where they defined three trustworthiness attributes based on mayer1995integrative and lee2004trust as "ability," "intention benevolence," and "process integrity" and highlighted the concept of trustworthiness cues. Trustworthiness cues are any information that can contribute to a person's trust assessment, including efforts for AI transparency (e.g., performance metrics, XAI and system design features, or model documentation) Liao2022, ekman2017creating.

§.§ Calibrating Trust in AI

By introducing trustworthiness as a property of the trustee, it is emphasized that trust should not exist for its own sake but requires justification. In light of this, lee2004trust have coined the term "trust calibration." Calibration refers to the correspondence between an individual's trust in a system and the system's trustworthiness lee2004trust. Within this framework, two types of mismatches can occur: either an individual's trust exceeds the system's trustworthiness, leading to misuse of the system (i.e., over-reliance Parasuraman1997), or the individual's trust falls short of the system's trustworthiness, leading to disuse (i.e., under-reliance Parasuraman1997). Ideally, individuals should exhibit calibrated trust, where the level of trust matches the trustworthiness of the system. More recently, wischnewski2023measuring have encouraged the research community to more explicitly focus on and increase calibrated trust. Further, jacovi2021formalizing introduced the notion of warranted and unwarranted trust in the context of AI. They refer to warranted trust as trust calibrated with trustworthiness; otherwise, trust is unwarranted if not calibrated with trustworthiness.

The notion of warranted and unwarranted trust brings about an interesting distinction - presuming an AI system is untrustworthy (e.g., has poor performance), not only is a person's trust unwarranted, but conversely distrust is warranted jacovi2021formalizing. In other words, if a system is untrustworthy, it may not be enough for people not to trust it, but desirable for people to actively distrust the system. jacovi2021formalizing argued that while the key motivation of XAI is commonly framed as increasing trust in AI systems, a more precise motivation should be to either increase trust in trustworthy AI or to increase distrust in untrustworthy AI. This distinction underlines the theoretical relevance of distrust and the need for its consideration in AI and XAI research. However, the AI and XAI research community seems to have mainly focused on trust scharowski2023distrust, and while this has provided important insights into how trust in AI can be developed and maintained, distrust has been relatively understudied, with only 6% of papers on human-AI interaction measuring and reporting distrust ueno2022scoping.

This unilateral perspective ignores decades of research in the area of interpersonal trust [e.g.,][]lewicki2006models, luhmann1979trust, sitkin1993explaining, saunders2014trust, mcknight2001trust, ou2009trust that has extensively explored the coexistence and independence of trust and distrust.
There are theoretical reasons for a potential distinction between trust and distrust as independent constructs rather than polar opposites mcknight2001trust. Indeed, some authors argue that "most trust theorists now agree that trust and distrust are separate constructs" [p. 42]mcknight2001trust.
Based on this theoretical work, a distinction between trust and distrust should be taken into consideration in human-AI interactions. Such insights from interpersonal trust research could inform our understanding of these constructs in the AI context, provided they can be measured appropriately and accurately. This would allow to evaluate not only warranted trust for trustworthy AI but also warranted distrust, calibrated with untrustworthy AI as proposed by jacovi2021formalizing.

§.§ Measuring Trust in AI

Trust in AI is measured in various ways vereschak2021evaluate, hoffman2023measures by both objective or subjective means mohseni2018multidisciplinary. Defining trust as an attitude lee2004trust implies that it should be viewed as a subjective psychological construct distinct from objective behavioral manifestations of trust, such as reliance lee2004trust, scharowski2022trust. This implies that studies that only measure trust-related behavior, such as reliance, do not genuinely measure trust scharowski2022trust, vereschak2021evaluate.

Conceptualizing trust as subjective leads to multiple methods to measure trust, including interviews, think-aloud protocols, and open-ended questions vereschak2021evaluate, mohseni2018multidisciplinary. Nevertheless, questionnaires are the primary source for the measurement of subjective trust ueno2022scoping, vereschak2021evaluate, with ueno2022scoping estimating that 89% of publications measure trust in AI via questionnaires. Questionnaires are a series of questions (i.e., questionnaire items) designed to measure a not directly observable psychological construct of interest devellis2017scale, hopkins1998educational.

Questionnaires should be distinguished from single-item questions that are also used to measure trust kohn2021measurement[e.g.,][]yu2017user but are generally less appropriate to study complex constructs loo2002caveat. Also, self-developed questionnaires are frequently employed to measure trust kohn2021measurement[e.g.,][]yin2019understanding, merritt2011affective, but these are questionable since they often lack a thorough design and validation process furr2011scale.
Furthermore, since self-developed questionnaires and single-item questions are often employed in a single study only, they usually do not allow comparing results across different studies flake2020measurement. For this reason, wischnewski2023measuring recommended using validated and standardized trust questionnaires that have undergone scrutiny to ensure their psychometric quality, including objectivity, reliability, and validity. However, this recommendation poses challenges for researchers who want to measure trust in AI, as no validated trust questionnaire in the context of AI exists. In the following, we discuss two scales that are currently used to assess trust in AI systems but have yet to be validated.

§.§.§ The Trust Between People and Automation Scale

Among trust questionnaires, the TPA scale by jian2000foundations is by far the most frequently used in human-AI research vereschak2021evaluate, wischnewski2023measuring, hoff2015trust, ueno2022scoping, kohn2021measurement. The TPA was developed 20 years ago and has been validated for the context of automation spain2008towards. Researchers adopting the TPA to measure trust in AI thus need to modify the questionnaire items to fit them to the AI context. vereschak2021evaluate estimated that more than half of all publications introduce such modifications to the original, validated questionnaires (e.g., changing "the system is dependable" to "the artificial intelligence is dependable"). However, terminological differences affect people's perceptions and evaluations of technology langer_CHI_22, and any modification of a questionnaire can undermine its reliability and raises the question of whether an adapted scale measures the intended construct. Consequently, after any modification, the psychometric quality of a questionnaire should be reassessed furr2011scale, juniper2009modified, which is rarely done vereschak2021evaluate.

The TPA consists of 12 items, with seven positively formulated items for trust and five items being negatively formulated, capturing distrust. However, because of the strong negative correlations between ratings of trust and distrust, the original authors concluded that trust is a single-dimensional construct, with trust and distrust as opposites on the two extremes of a continuum. spain2008towards, who independently validated the TPA in the context of automation, challenged this single-dimensional notion of trust and showed that trust and distrust formed two independent factors.
When using the TPA, past research has followed one of two approaches: either to re-code the five negatively formulated items of the scale before data analysis, resulting in a single trust score measured by the scale, or to not re-code items and create two separate scores; one score using the first five items for distrust and a second score using the seven remaining items for trust ueno2022scoping. This also reflects the uncertainty of whether the TPA measures a single construct (i.e., trust) or two distinct constructs (i.e., trust and distrust).

§.§.§ The Trust Scale for the AI Context

More recently, hoffman2023measures designed an AI trust questionnaire, the TAI. The TAI is based on existing trust scales, including the TPA jian2000foundations, and consists of eight items, with one negatively formulated item, all presumably capturing trust. However, the authors did not provide psychometric evidence of validity for the TAI. Compiling items from validated questionnaires to develop a new scale does not guarantee its psychometric quality and brings similar challenges and requirements as questionnaire modification. Therefore, independent validation of the TAI would be a valuable and necessary contribution towards more standardized measures of trust in AI.

A first effort to validate the TPA and TAI in the AI context was the preliminary work by perrig2023trust. Their findings supported the two-factor structure of trust and distrust for the TPA. Furthermore, their findings suggested that the TAI is better suited to measure trust, although removing some items was required to achieve a good fit for the TAI and an acceptable fit for the TPA perrig2023trust.
However, their study was not a dedicated validation study and thus limited to one specific low-risk AI application (i.e., real estate valuation domain). Additionally, the AI system used in their study only exhibited trustworthy behavior. Hence, the researchers could only investigate the psychometric quality of the TPA and TAI in a setting where participants interacted with trustworthy AI. Subsequently, there is no research examining the scales' performance in the context of untrustworthy AI.

The present study aims to expand on their work and seeks to overcome its limitations in three ways:
First, the validation of the scales is expanded to two additional AI application areas - chatbots and automated vehicles (AV), representing current AI systems operating in real-world environments.
Second, we investigate both low-risk (chatbots) and high-risk (AVs) scenarios, thereby considering vulnerability and risk.
Third, we distinguish between trustworthy AI and untrustworthy AI to assess criterion validity more comprehensively.
For this, our study drew on the trust model by mayer1995integrative, paralleling the approach by Esterwood2023, in manipulating the trustworthiness of AI through performance variations.
Specifically, we created two experimental conditions. In one condition, participants were presented with a high-performing trustworthy AI, without failures, eliciting trust. Conversely, in the other condition, participants were exposed to low-performing untrustworthy AI, with failures, intended to evoke distrust.

§ STUDY OBJECTIVES AND HYPOTHESES

Motivated by the need for adequately validated and standardized measures for trust in the context of AI, we set out to validate the TPA and TAI in a pre-registered, high-powered online experiment. We formulated the following objectives:

Objective 1: Conducting a psychometric evaluation of the TPA scale by jian2000foundations in the context of AI.

Objective 2: Conducting a psychometric evaluation of the TAI by hoffman2023measures.

In order to meet these objectives, the following methods of psychometric evaluation were used:
For the quality of the individual items, several metrics were considered, namely item descriptive statistics, item difficulty and variance, discriminatory power, and inter-item correlations.
Concerning construct validity and investigation of the scales' theoretical models, confirmatory factor analysis and, if needed, exploratory factor analysis were used. For convergent and divergent validity, we considered correlations with a set of additional measures. Here, we were interested in the relationship of trust and distrust – if support for a two-factor solution to the TPA was found – to the related constructs of positive affect, negative affect, and situational trust, which is similar but distinct from general trust.
For reliability, we calculated indicators of internal consistency, namely coefficients $\alpha$ cronbach1951coefficient and $\omega$ mcdonald1999test.

Concerning scale ratings, taken as indicators of the scale's criterion validity and a manipulation check for our stimuli, we formulated the following pre-registered hypotheses:

* H1a: Ratings for the TPA overall score will be significantly higher for the trustworthy condition than the untrustworthy condition.[Note that in the pre-registration, we referred to the two conditions as "trust" and "distrust." In writing this manuscript, however, we have decided that it is more appropriate to refer to the condition eliciting trust as "trustworthy" and distrust as "untrustworthy", which is more consistent with related work.]

* H1b: Ratings for the TPA trust score will be significantly higher for the trustworthy condition than the untrustworthy condition.

* H1c: Ratings for the TPA distrust score will be significantly higher for the untrustworthy condition than the trustworthy condition.

* H2: Ratings for the TAI score will be significantly higher for the trustworthy condition than the untrustworthy condition.

* Manipulation check 1: Ratings of risk will be significantly higher for the automated vehicle application compared to the chatbot application.

* Manipulation check 2: Ratings of risk will be significantly higher for the untrustworthy condition compared to the trustworthy condition.

No hypotheses were formulated regarding any possible differences in the ratings of the TPA and TAI between the two areas of application (chatbot vs. automated vehicle).

§ METHODS

A 2x2 mixed design in form of an online experiment was conducted to validate the TPA and the TAI.
In order to reach the number of participants necessary for a high-impact validation study we used a scenario-based approach, following prior work on trust kapania2022because, Jakesch, Binns.2018, scharowski_2023, holthausen2020sts_ad, schaefer2016measuring. Participants were presented with two pre-recorded videos, each accompanied by a brief description of what they were about to see. The experimental manipulation consisted of two independent variables.

The first independent variable was the type of AI system presented, with the videos either showing an interaction with an AV or a chatbot (i.e., application). The second independent variable was whether the video displayed a trustworthy or an untrustworthy AI (i.e., condition). The order of all of the videos was randomized. Thus, all participants were in the trustworthy condition for one application area while being in the untrustworthy condition for the other application, forming a crossover design with four scenarios (see <ref> for a visualization of the stimuli). After each video, participants filled out the TPA, and TAI, alongside other related survey scales, namely the Situational Trust Scale [STS,][]dolinek2022sts or the Situational Trust Scale for Automated Driving [STS-AD,][]holthausen2020sts_ad and the Positive and Negative Affect Schedule [PANAS,][]watson1988panas.
The study was approved by the ethics committee of the corresponding author's university and pre-registered on OSF (<https://doi.org/10.17605/OSF.IO/3EU4V>).

An illustration of the 2x2 online experiment stimuli by condition (trustworthy vs. untrustworthy) and application (chatbot vs. automated vehicle), constituting four scenarios in total.

§.§ Stimuli

Participants were asked to watch two out of the four videos depicting an interaction with AI, one each showing an automated vehicle and a chatbot displaying either trustworthy or untrustworthy behavior. A brief description of the scenario accompanied these videos.
In the trustworthy condition, one video showed an AV without any automation failure driving safely through an urban environment. The other video featured a chatbot, providing truthful answers to basic knowledge questions (e.g., "a mouse is smaller than an elephant").
In contrast, in the untrustworthy condition, the videos showed the following failures. Firstly, a staged video of an AV that approaches a crosswalk and seemingly not slowing down for a pedestrian attempting to cross the road [material taken from][]holthausen2020sts_ad. Secondly, a chatbot interaction, where the chatbot gives incorrect answers to basic knowledge questions such as “the number 50 is bigger than 5000” or “the sky has the color brown.” Based on the potential consequences of failures in these two AI interactions, we defined the AV application area as high-risk and the chatbot application area as low-risk.

§.§ Participants

We recruited 1500 participants over Prolific, a crowd-sourcing platform recently demonstrated to deliver high data quality douglas2023data, peer2022data.
To be eligible for the study, participants had to be current residents of the United States of America (USA) and over 18 years of age. Those who completed the study were compensated £1.50 for their efforts.
Using rules of thumb for sample sizes in structural equation modeling, recommending at least ten but ideally twenty observations per estimated parameter kline2016principles, the goal was to recruit at least 700 responses for each scenario (condition x application). Recruiting 1500 participants gave us additional leverage if participants were excluded from data analysis and further allowed us to explore more complex models should they become necessary.

Data cleaning was carried out in line with recommendations by bruehlmann2020quality, removing participants with incorrect responses to two instructed response items or with negative responses to a self-reported data quality item.
Based on self-reported data quality, six observations were removed. Another three participants with an incomplete or interrupted survey were removed, as well as six participants who did not report the USA as their current country of residence.
After data cleaning, 1485 participants remained, with 2970 complete responses to the measures. Of the participants, 726 were women, 726 were men, and 25 were non-binary people. Two participants preferred to self-describe, and six chose not to specify their gender. The mean participant age was 42.98 years ($SD = 13.95, min = 18, max = 82$).
Participants were spread evenly across the four scenarios: 738 responses for the trustworthy chatbot video, 747 for untrustworthy chatbot, 747 for trustworthy AV, and 738 for untrustworthy AV.

§.§ Procedure

On the first page of the survey, participants provided their informed consent. Next, they were given instructions for the task to be completed. Participants were randomly assigned to one of the four videos and asked to watch the video at least once (which was verified by the survey tool).
After watching the first video, participants filled out the TPA and the TAI, followed by additional measures.
Participants were then shown the second video, this time for the other condition and application, before responding again to all measures.
Finally, participants provided demographic information (age, gender, country of residence) before having the opportunity to give general open feedback and being redirected to Prolific for compensation. To ensure sufficient response quality, the survey included two instructed response items curran2016methods embedded among the survey scales and a single item for self-reported data quality meade2012identifying at the end of the survey.
After the survey, participants were debriefed that all videos were staged and that at no point an individual was in any real danger or at risk.
Completing the study took participants an average of 11.38 minutes ($SD = 6.07, min = 3.68, max = 49.03$).
Prior to data collection, we conducted a small-sample pre-study ($N=70$) to test the procedure and tasks of the online survey. Based on the insights from this study, some minor technical adjustments were made.

§.§ Measures

Participants responded twice to all items of the TPA and TAI and additional scales to measure convergent and divergent constructs, once for each scenario they were assigned to. The order in which the TPA and TAI were presented was randomized, as was the order among the other scales.
The supplementary materials on OSF contain the exact wording of all items used. The internal consistency for all measures was examined using coefficients $\alpha$ cronbach1951coefficient and $\omega$ mcdonald1999test, yielding good results for all scales (see the section on reliability results below for the TPA and TAI, and OSF for the other scales).

§.§.§ TPA

Participants responded to all 12 items of the TPA jian2000foundations. Answers were collected on the proposed seven-point Likert-type response scale ranging from 1 ("not at all") to 7 ("extremely").
Responses to the five negatively formulated items of the scale were re-coded prior to data analysis, as theoretically implied by the original authors jian2000foundations and in line with prior work spain2008towards, ueno2022scoping.
All items were used in their original form, except for replacing the word "system" with the word "AI" (e.g., "I am confident in the AI").

§.§.§ TAI

For the TAI hoffman2023measures, responses to all eight items were collected using the recommended five-point Likert-type response scale ranging from 1 ("I disagree strongly") to 5 ("I agree strongly"). The only negatively formulated item of the scale (i.e., "I am wary of the AI") was re-coded prior to data analysis.
For the TAI, items were also adapted to the AI context by replacing the word "tool" with "AI" (e.g., "The outputs of the AI are very predictable").

§.§.§ STS and STS-AD

Depending on the application area (i.e., chatbot or AV), participants either responded to the STS dolinek2022sts or the STS-AD holthausen2020sts_ad.
The STS-AD is a six-item scale measuring peoples' situational trust in an automated driving context. In contrast, the STS is a generalized eight-item version of the STS-AD, assessing situational trust in AI systems in general.
Responses to both scales were collected on the same seven-point Likert-type response scale ranging from 1 ("Fully disagree") to 7 ("Fully agree"), and mean values across all items of the respective scale were formed for the analysis.
The STS-AD was chosen because the original work on the scale demonstrated that the scale measures a "situational trust" factor that is related to but distinct from "general trust" measured with the TPA. The STS was chosen as an alternative to the STS-AD in the chatbot application area to measure situational trust.

§.§.§ PANAS

To measure people's positive and negative affect experienced while seeing the AI interaction, we used the PANAS watson1988panas. The PANAS consists of 20 items, ten for positive affect and ten for negative affect. Responses were collected on a five-point Likert-type response scale raining from 1 ("Very slightly or not at all") to 5 ("Extremely"), and mean values were formed across positive and negative items respectively to form scores for "positive affect" and "negative affect."
The PANAS was chosen because trust and distrust are assumed to cause different emotional responses luhmann1979trust, lewicki1998trust. While trust is associated with more positive affect, distrust is associated with more negative affect.

§.§.§ Single Item for Risk

Finally, we employed a single item for risk ("How risky did you consider the scenario in the video to be?") to which participants responded on a slider response scale from 0 ("Not at all risky") to 100 ("Extremely risky").
We used this single item to measure risk because it is a key element rousseau1998not, vereschak2021evaluate, hoff2015trust, castelfranchi2010trust and prerequisite for trust to exist jacovi2021formalizing. Although we generally advise against single items, we decided to employ one to assess risk in this case as it served solely as a manipulation check for our stimuli and because it was challenging to arrive at an informed decision about which risk questionnaire was appropriate to use for the contexts under investigation.

§ RESULTS

The analysis focused on different procedures to assess the psychometric quality of the TPA and TAI. Results were obtained using the statistical software R [][version 4.3.0]rcore. The complete analysis can be found in the supplementary materials on OSF.

§.§ Manipulation Check

To verify the experimental manipulation, we performed a two-way analysis of variance (ANOVA) for the risk ratings with the factors application area (AV vs. chatbot) and condition (trustworthy vs. untrustworthy).
Results showed that the application area had a statistically significant effect on the risk rating (manipulation check 1: $F(1, 2967) = 1426$, $p < .001$, $\eta^2 = .22$), with a higher risk rating for the AV ($M = 64.09, SD = 34.49$) compared to the chatbot application ($M = 27.22, SD = 34.72$). Concerning condition, there also was a significant difference (manipulation check 2: $F(1, 2967) = 1963$, $p < .001$, $\eta^2 = .31$), with a higher risk rating for the untrustworthy ($M = 67.44, SD = 36.49$) compared to the trustworthy condition ($M = 23.87, SD = 28.17$).
We further calculated two Wilcoxon rank sum tests because assumptions for ANOVA were not met (normality, homogeneity of variance). Results were in line with those of the ANOVA, showing a significant difference in risk between the conditions and the applications ($p < .001$ for both tests).
We thus concluded that the manipulation was successful.
Separated by the four scenarios, mean risk ratings were as follows: $39.27$ $(SD = 28.83)$ for the trustworthy AV, $89.20$ $(SD = 17.26)$ for the untrustworthy AV, $8.28$ $(SD = 16.52)$ for the trustworthy chatbot, and $45.94$ $(SD = 37.72)$ for the untrustworthy chatbot.

§.§ Item Analysis

We started with the psychometric analysis of the individual items' quality, calculating descriptive statistics, item difficulty and variance, discriminatory power, and inter-item correlations separately for the 12 TPA items and the eight items of the TAI.
Item analysis was performed across the four scenarios (condition x application), as well as the aggregated overall data.
Results for both the TPA and TAI were inconspicuous for most of the items (see OSF for the complete item analysis).
Consequently, we decided to work with the overall data across all scenarios for the subsequent analysis.

§.§ Confirmatory Factor Analyses

Concerning construct validity, we used confirmatory factor analysis (CFA) to investigate the proposed models of the two trust scales. Based on hu1999cutoff, model fit was judged using the following criteria: Low $\chi^2$ value and $p > .05$ for the $\chi^2$ test, $RMSEA < .06$, $SRMR \leq \ .08$, and $.95 \leq \ CFI \leq \ 1$.
Because multivariate normality of the TPA and TAI data was not given, shown by Henze-Zirkler tests henze1990class and Mardia's tests mardia1970measures, we used a robust maximum likelihood estimator for all CFAs.
The $\chi^2$ test was significant for all CFAs, which was to be expected given that the test is influenced by larger sample sizes ($> 200$) and departures from multivariate normality whittaker2022beginner. We thus focused on the other indicators to judge model fit.
Starting with the TPA, CFA results showed that the originally proposed single-factor model did not fit the data well, with all indices outside of the recommended values [$\chi^2(54) = 2857.47$, $p < .001$, $RMSEA = .157$, $SRMR = .085$, $CFI = .887$].
Concerning the TAI, CFA results mostly supported the suggested single-factor model [$\chi^2(20) = 258.81$, $p < .001$, $RMSEA = .073$, $SRMR = .021$, $CFI = .986$], with only the RMSEA slightly above the recommended value but below .08, which can still be considered acceptable maccallum1996power.
Therefore, we decided to perform further analyses, exploring alternative models for the TPA while concluding that no such efforts were necessary for the TAI.

§.§ Exploratory Factor Analysis - TPA

Given the sub-optimal model fit for the TPA, we decided to conduct an exploratory factor analysis (EFA) to search for alternative models with a better fit to the collected data.
Because multivariate normality was not given, we chose a principal axis factoring extraction method. Furthermore, we used an oblique rotation method because we expected correlations among possible factors.
For the interpretation of the factor loadings, we used the $.40 - .30 - .20$ rule howard2016review, which states that items should load at least $.40$ on their primary factor, with no cross-loading $> .30$ on another factor, and a difference of at least $.20$ between the primary and any secondary loading. Regarding communality, values $< .50$ were considered sub-optimal hair2010multivariate.

A significant Bartlett's test for sphericity ($\chi^2(66) = 35089.26$, $p < .001$) and an adequate Kaiser-Meyer-Olkin test (all $> .80$) indicated that the prerequisites for EFA were given.
Parallel analysis and a scree-plot were consulted to determine the number of factors, suggesting a two-factor solution.
Factor loadings and communalities for the two-factor EFA with all 12 TPA items are presented in <ref>.
Regarding explained variance, the first factor explained $45.5\%$, while the second factor explained $19.3\%$.

Factor loadings $> .20$ for the two-factor EFA of the TPA.
No.     Item                                                            PA1     PA2     h2
1       The AI is deceptive (R)                                                                     .75              .64

2       The AI behaves in an underhanded manner (R)                                                 .87              .63

3       I am suspicious of the AI's intent, action or, outputs (R)                                  .69              .67

4       I am wary of the AI (R)                                           .46              .42              .63

5       The AI's actions will have a harmful or injurious outcome (R)              .32              .53              .60

6       I am confident in the AI                                                   .92                               .91

7       The AI provides security                                                   .92                               .73

8       The AI has integrity                                                       .84                               .60

9       The AI is dependable                                                       .87                               .87

10      The AI is reliable                                                         .88                               .91

11      I can trust the AI                                                         .92                               .90

12      I am familiar with the AI                                         .56                               .25
5lc]@l@ Note: Problematic items are marked in bold. PA1/PA2 = factor loadings; h2 = communality.

Reverse-coded items are marked with (R).

Based on the results, we concluded that the removal of item 4 might result in an improved version of the scale because the item showed equally high loadings on both factors. Furthermore, item 12 was also conspicuous due to low communality and a substantially lower loading than the other items.
While item 5 showed a cross-loading slightly higher than recommended on a secondary factor, we decided not to remove it due to a high primary loading and an adequate difference in loadings between the primary and secondary factors.

§.§ Alternative Confirmatory Factor Analysis - TPA

Based on the results of the EFA, we tested an alternative two-factor model for the TPA without items 4 and 12.
The two-factor version resulted in an improved model fit [$\chi^2(34) = 903.26$, $p < .001$, $RMSEA = .110$, $SRMR = .053$, $CFI = .961$], with the SRMR and the CFI favoring the model and a substantially lower $\chi^2$-value compared to the single-factor model.
Therefore, we concluded that the TPA should be used with a two-factor model and without items 4 and 12.
For all subsequent analyses, we thus worked with this two-factor solution separating trust and distrust.

§.§ Reliability

Following recommendations by dunn2014alpha, we calculated both coefficients $\alpha$ cronbach1951coefficient and $\omega$ mcdonald1999test, including 95% confidence intervals, to assess the TPA's and TAI's reliability.
Results are presented in <ref>, showing that the TAI and the alternative TPA were of good to excellent internal consistency [$> .80$,][]george2019ibm.

Coefficients $\alpha$ and $\omega$ for the two trust scales, including 95% confidence intervals.

Scale                            $\alpha$      $\omega$

TPA (trust items, without 12)             .96 [.96, .97]     .97 [.96, .97]

TPA (distrust items, without 4)           .86 [.85, .87]     .86 [.85, .87]

TAI (trust)                               .95 [.94, .95]     .95 [.95, .95]

§.§ Convergent and Divergent Validity

To assess convergent and divergent validity, we calculated Pearson's product-moment correlations reflecting the relationship between the two trust scales and the related measures.
Based on the model identified in the EFA and alternative CFA, we refrained from forming a single trust score for the TPA across all items.
Rather, we formed two distinct scores for trust and distrust separately, reflecting the two-factor model.
In particular, we calculated a "TPA trust" score based on the mean of items 6 to 11 and a "TPA distrust" score based on the mean across the non-reversed values of items 1, 2, 3, and 5.
For the TAI, we calculated a single mean "trust" score by averaging ratings across all items after the reversal of the negatively formulated item 6.
Based on past work lewicki2006models, perrig2023trust and results of the pilot study, we expected the following correlations among the measured variables:

* Positive correlations between the mean across TPA trust items ("TPA trust") and the mean across all TAI items ("TAI trust").

* Weaker or negative correlations of the mean across the non-reversed TPA distrust items ("TPA distrust") with TPA trust and TAI trust.

* Positive correlations of situational trust with TPA trust and TAI trust, and weaker or negative correlations with TPA distrust.

* Positive correlations of positive affect with TPA trust and TAI trust, and weaker or negative correlations with TPA distrust. For negative affect, we expected a mirrored pattern.

All correlations are presented in <ref>.
Results were as anticipated in the pre-registration, supporting the convergent and divergent validity of the two scales.

Correlations between the TPA, TAI, and the other measures, including 95% confidence intervals.

TPA trust        TPA distrust     TAI trust

TPA distrust                     -.67 [-.69, -.65]         -                         -

TAI trust                        .93 [.92, .93]            -.70 [-.72, -.68]         -

STS-AD/STS situational trust     .86 [.85, 87]             -.75 [-.76, -.73]         .88 [.88, .89]

PANAS positive affect            .40 [.37, .43]            -.14 [-.18, -.11]         .37 [.34, .40]

PANAS negative affect            -.32 [-.36, -.29]         .46 [.44, .49]            -.37 [-.40, -.34]

4lc]@l@ Note: Mean scores for the SDS and SDS-AD were combined into one variable. All

correlations were significant at $p < .001$.

§.§ Criterion Validity

Next, we investigated how the scores of the TPA and TAI differed between the four scenarios, addressing the pre-registered hypotheses. For this, we used two-way ANOVAs to test if the mean ratings for the scales differed significantly depending on the condition (trustworthy vs. untrustworthy) or the application area (AV vs. chatbot).
Descriptive statistics separated by the four scenarios are presented in <ref>, while the statistics for the condition and the application are provided in the supplementary materials.
Because we did not calculate an overall trust score for the TPA, we chose not to report results concerning hypothesis H1a in this manuscript.

Descriptive statistics for all collected measures, separate per condition.

2cChatbot trustworthy          2cChatbot untrustworthy         2cAV trustworthy         2cAV untrustworthy

2-3 5-6 8-9 11-12
Construct               Mean     SD         Mean     SD         Mean     SD         Mean     SD

TPA trust                        4.53     1.33            1.78     1.27            4.13     1.43            1.86     1.23

TPA distrust                     2.13     1.23            4.60     1.64            2.63     1.24            4.69     1.37

TAI trust                        3.55     0.78            1.73     0.81            3.14     0.89            1.65     0.80

SDS-AD situational trust         -        -               -        -               4.58     1.20            1.52     0.96

SDS situational trust            5.49     0.90            2.27     1.18            -        -               -        -

PANAS positive affect            2.65     0.97            2.36     0.87            2.73     0.95            2.41     0.78

PANAS negative affect            1.17     0.42            1.58     0.79            1.44     0.63            2.25     0.98

12lc]@l@ Note: Responses could range from 1 to 5 for the TAI and from 1 to 7 for all other measures.

H1b; higher TPA trust score for the trustworthy condition than untrustworthy condition.
A first two-way ANOVA investigating the effect of the condition and application area on the TPA trust score revealed a statistically significant effect for condition ($F(1, 2967) = 2662.30$, $p < .001$, $\eta^2 = .47$) and for application ($F(1, 2967) = 11.10$, $p < .001$, $\eta^2 < .01$).
Results thus supported H1b with a large effect of the condition on the TPA trust score but no substantial effect for the application area.

H1c; higher TPA distrust score for the untrustworthy condition than trustworthy condition.
Concerning the TPA distrust ratings, a second two-way ANOVA revealed a significant effect for the condition ($F(1, 2967) = 1981.83$, $p < .001$, $\eta^2 = .40$) and for the application ($F(1, 2967) = 32.77$, $p < .001$, $\eta^2 < .01$).
Results thus favored H1c, suggesting a large effect of the condition on the TPA distrust score and a negligible effect for the application area.

H2; higher TAI score for the trustworthy condition than untrustworthy condition.
Regarding the TAI score, a third two-way ANOVA showed a significant main effect for condition ($F(1, 2967) = 2994.73$, $p < .001$, $\eta^2 = .50$) and for application ($F(1, 2967) = 65.61$, $p < .001$, $\eta^2 = .01$).
Results thus supported H2. The effect size was large for the condition but small for the application area.

Furthermore, we calculated a set of Wilcoxon rank sum tests because the normality and homogeneity of variance assumptions for the ANOVAs were not met.
Results were comparable to those of the ANOVAs, with significant effects of condition on the TPA trust score, TPA distrust, and the TAI score ($p < .001$ for all tests). In contrast, the tests suggested no significant differences between the application areas for the TPA trust score, but for TPA distrust and the TAI (see supplementary materials for details).

§.§ Model Stability Across Scenarios

Finally, we investigated the stability of the model fit for the two trust scales across the four scenarios (condition x application), using this as an indicator of the scales' measurement invariance.
For this, we calculated eight CFAs, one for each scenario and scale, employing the alternative version of the TPA (i.e., a two-factor model without items 4 and 12) and the originally proposed single-factor model confirmed for the TAI.
All results from the CFAs are presented in <ref>.
Concerning the TPA, the model fit indices only supported the model in the untrustworthy chatbot scenario, except for the RMSEA, which was slightly above the ideal cutoff.
For the TAI, the model was supported by all fit indices in all four scenarios except for the RMSEA in the trustworthy chatbot scenario.

Fit indices for CFA models of the trust scales, separated by scenario (condition x application).

Model              $\chi^2$     df     p-value $\chi^2$     RMSEA     SRMR     CFI

AV trustworthy              309.47       34            $p < .001$                         .120            .087            .934

AV untrustworthy            239.95       34            $p < .001$                         .109            .137            .940

Chatbot trustworthy         236.34       34            $p < .001$                         .110            .078            .943

Chatbot untrustworthy       112.89       34            $p < .001$                         .064            .048            .982

AV trustworthy              58.14        20            $p < .001$                         .059            .028            .986

AV untrustworthy            42.70        20            $p = .002$                         .050            .025            .990

Chatbot trustworthy         118.50       20            $p < .001$                         .089            .038            .959

Chatbot untrustworthy       39.44        20            $p = .006$                         .047            .027            .992

7lNote: Robust values are reported wherever possible.

§ DISCUSSION

Motivated by the need for standardized and validated scales to measure trust in AI, the present work investigated the psychometric quality of two trust measures. First, we investigated the TPA jian2000foundations as it is the most commonly used questionnaire to measure trust in AI. Second, we assessed the recently introduced TAI hoffman2023measures, because it is explicitly intended for the AI context. In a pre-registered 2x2 within-subject online experiment, 1485 participants watched two videos showing interactions with AI. Each video featured an interaction with one of two AI application areas, a chatbot or an autonomous vehicle, and portrayed the AI under one of two conditions, either displaying trustworthy or untrustworthy behavior. Subsequently, participants rated the interactions using the TPA, TAI, and related measures.

As hypothesized and pre-registered, results indicated that both the TPA and the TAI could differentiate between the two conditions. Specifically, in the condition where participants were presented with trustworthy AI, we observed significantly higher trust scores (supporting H1b and H2) and significantly lower distrust scores (supporting H1c), compared to the condition with untrustworthy AI.
We took these results not only as an indication of the scales' criterion validity but, together with the significantly higher risk ratings in both the AV application area and untrustworthy AI condition, as additional evidence of a successful experimental manipulation.
Regarding convergent and divergent validity, the relationships between the ratings of the TPA, TAI, and related measures were consistent with our expectations. Namely, trust scores from the TPA and TAI had strong positive correlations with one another. Furthermore, the two trust scores also correlated positively with situational trust and positive affect, while correlating negatively with negative affect. In contrast, the pattern was reversed for the TPA distrust score. Results thus demonstrate that distrust and trust are associated with different affects, as proposed by lewicki1998trust. These results further suggest that trust and distrust are two distinct constructs.
Results also showed good to excellent reliability for the TAI and the TPA, as indicated by both internal consistency coefficients.
In summary, our results support the reliability, as well as convergent, divergent, and criterion validity of both scales.

Results of the CFA further supported the TAI's underlying theoretical model, providing strong evidence for its construct validity.
However, concerning the TPA, the results of the factor analyses were more nuanced, raising questions regarding its theoretical model.
While the scale demonstrated good psychometric quality for a majority of indicators considered, these findings alone do not necessarily guarantee an accurate measurement of the underlying theoretical construct if construct validity is not given.
Thus, while the results suggest that the TPA accurately measures something, without validity, it remains unclear if the TPA truly measures trust, rather than other related constructs (e.g., trustworthiness) moosbrugger2020testtheorie.
In the following sections, we will discuss the implications of the results for the two scales separately before elaborating on more general ramifications of measuring trust and distrust for AI research.
We start with the TAI, followed by the more complex results of the TPA.

§.§ Measuring Trust with the TAI

For the TAI, findings supported the initially proposed single-factor solution for measuring trust.
This model performed well both in the combined data and across all four scenarios, irrespective of the application area (AV vs. chatbot) or the condition (trustworthy vs. untrustworthy). Combined, these findings speak in favor of using the TAI as a single-dimensional measure of trust, with "low trust" and "high trust" at opposite ends of a continuum.
However, the resulting single-factor solution also implies that the TAI can only account for trust.
Considering the main motivations of XAI as outlined by jacovi2021formalizing, the TAI thus falls short in addressing warranted distrust for untrustworthy AI.

§.§.§ Recommended Use for the TAI

Researchers and practitioners interested in measuring trust in AI can use the TAI to measure trust as a single-dimensional construct, with "low trust" and "high trust" at the two ends of a continuum.
To maintain reliability and validity, the questionnaire should be adopted as closely as possible to the version validated in the present work. This includes using the exact item wording provided in our supplementary materials and a five-point Likert-type response scale with the corresponding response options.
After data collection, researchers should first reverse the score for the negatively formulated item 6. Subsequently, an overall trust score can be computed by calculating the mean across all items.

§.§ Measuring Trust and Distrust With the TPA

Concerning the TPA, CFA results did not support the originally proposed single-factor model.
The subsequent EFA clearly suggested a two-factor solution, differentiating between trust and distrust. These results are in line with previous work on the TPA spain2008towards, perrig2023trust.
To improve model fit, we removed items 4 and 12. However, the psychometric performance of the TPA still presents room for improvement.
Some of the fit indices remained sub-optimal for the overall data, and the scale's model fit was inadequate within three out of the four scenarios.
Having said that, the identified two-factor version of the TPA can distinguish between trust and distrust, making it possible to measure warranted trust and warranted distrust.
This theoretical distinction is a major advantage of this questionnaire.
To reach the full potential of the TPA, however, we call for more research efforts on the scale and specifically the formulation of additional items for distrust.
The TPA includes more items for the factor trust than for distrust in both the original version (five items for distrust, seven items for trust) and the alternative version identified in the present work (six items for trust, four items for distrust). This item imbalance could lead to a less accurate measurement of distrust, with fewer items potentially not covering essential aspects of the construct.

§.§.§ Recommended Use for the TPA

In light of our empirical evidence, we strongly recommend that researchers and practitioners do not work with the original single-factor model proposed by jian2000foundations when using the TPA in the context of AI.
Instead, we suggest using a two-factor structure that accounts for both trust and distrust.
Accordingly, researchers should average the distrust items of the TPA to a composite distrust score without any reversal while using the remaining items to calculate a mean trust score.
We strongly advise against aggregating all items into an overall score or re-coding the negatively formulated items, as the identified theoretical model does not support such procedures.
Regarding item removal to improve the scale's quality, both the present work and previous research perrig2023trust advocate for removing item 12. Additionally, we suggest removing item 4 when applying the scale in the context of AI.
However, despite item removal, a consistent model fit and, thus, adequate construct validity was not readily achieved for the TPA.
Researchers working with the TPA should investigate the quality of the scale prior to interpreting the data. If such investigation is not reasonable (e.g., due to a small sample size), we recommend sharing the data so that other researchers can investigate the TPA, for example by aggregating data from multiple research projects. Using the TPA in line with these recommendations allows for trust and distrust to be measured independently, with the added value and opportunities for human-AI research being discussed in the following.

§.§ Towards a Two-Dimensional Understanding of Trust and Distrust in AI?

The presented results for a two-factor structure for the TPA align well with prior research on the scale in the domain of automation spain2008towards and work from interpersonal trust emphasizing the importance of distrust lewicki2006models, luhmann1979trust, sitkin1993explaining, saunders2014trust, mcknight2001trust, ou2009trust, thus providing further empirical evidence for trust and distrust as two distinct and independent constructs.
While lewicki1998trust proposed that trust was based on more positive emotional affect and distrust on more negative affect, our results demonstrated that trust correlated positively with positive affect and negatively with negative affect, whereas this was the opposite for distrust. This provides additional support for a two-dimensional conceptualization of trust, challenging the unilateral perspective on trust.
However, our work should not be understood as a conclusive verdict on whether trust is in fact one- or two-dimensional, as our research design cannot conclusively resolve this question.
In the following, we nevertheless want to contextualize our findings in the broader theoretical discourse from interpersonal trust research and outline possible implications of a two-dimensional consideration of trust and distrust to inspire future research on human-AI trust.

Beyond distrust being associated with stronger emotional reactions lewicki1998trust and more negative affect (e.g., fear, skepticism, cynicism) than trust mcknight2001trust, both constructs help to navigate uncertainty and complexity lewicki1998trust.
Trust reduces complexity by compelling a person to take action that exposes them to risk (i.e., undesirable outcomes are removed from consideration to form positive expectations kroeger2019unlocking), while distrust reduces complexity by compelling a person to take protective action to reduce risk (i.e., undesirable outcomes are accentuated in consideration to form negative expectations kroeger2019unlocking) benamati2006trust.
In summary, an argument can be made that both the antecedents (e.g., the associated affect) and the consequences (e.g., the resulting function) of trust and distrust are distinct cacioppo1994relationship, lewicki1998trust, mcknight2001while, chang2013antecedents.

Drawing upon their proposed affectual and emotional differentiation between trust and distrust, lewicki1998trust developed a 2x2 framework with trust on the y-axis and distrust on the x-axis. This framework spans from "low trust/distrust" to "high trust/distrust" and provides an explanatory approach for the simultaneous and seemingly contradictory coexistence of trust and distrust.
In cases of low trust and low distrust, judgments about the trustworthiness or untrustworthiness of the trustee are still being formed lewicki1998trust. The trustor thus lacks a basis for either trust or distrust lewicki1998trust and only over time do judgments develop.
A practical and simplified example of such an interaction within the realm of AI might be a person encountering a chatbot for the first time. This person has no prior experience with the capabilities of large language models and, thus, no foundation to trust or distrust the chatbot.

Situations characterized by high trust and low distrust stem from predominantly positive experiences with the trustee. Contradictory evidence that could inform untrustworthiness is often disregarded or considered unimportant lewicki1998trust.
Such a case could include, for our example, that the person frequently observed the chatbot's high capabilities in generating poetry. While trust is warranted jacovi2021formalizing and calibrated lee2004trust for these tasks, the individual might over-rely Parasuraman1997 on the chatbot for other tasks, where distrusting and not relying on the chatbot would be more appropriate (e.g., providing accurate scientific literature).

With low trust and high distrust, negative experiences with the trustee predominate, reinforcing distrust. The trustor invests substantial resources in monitoring lewicki1998trust.
Following our example, the individual could be disappointed by a chatbot's inability to provide accurate scientific literature. They may actively avoid using the chatbot or monitor it more closely, double-checking its responses.
This could lead to warranted distrust jacovi2021formalizing calibrated with the AI's untrustworthiness lee2004trust, jacovi2021formalizing for the given task, but potentially causing disuse lee2004trust and under-reliance Parasuraman1997 when trusting and relying on the chatbot would be appropriate.

Finally, in situations of high trust and high distrust, the experience with the trustee is balanced, having both perceived trustworthy and untrustworthy behavior. The trustor effectively interacts with the trustee in certain (trusted) tasks but not in other (distrusted) tasks lewicki1998trust.
Returning to our example, the person has learned to trust the chatbot for tasks aligned with its trustworthiness (i.e., capability to generate poems) and to distrust the chatbot for tasks where distrust matches its untrustworthiness (i.e., incapability to provide accurate scientific literature). The person utilizes the chatbot to write poems but always double-checks its scientific references, showing both calibrated trust and distrust.
This last case seems to be the most preferable, where both trust and distrust are warranted and calibrated with the AI's trustworthiness or untrustworthiness.

Conceptualizing and reasoning about trust and distrust in such a way allows for addressing the two key motivations of XAI as outlined by jacovi2021formalizing: to increase trust in trustworthy AI and distrust in untrustworthy AI.
Within the confines of a single-dimensional trust conception, where one overall score is formed, it is not possible to determine whether "low trust" arises from actual distrust or from a lack of knowledge regarding the trustworthiness or untrustworthiness of the AI victor2009gradual.
Therefore, a two-dimensional understanding of trust and distrust offers additional insights, enabling practitioners and researchers to either increase the trustworthiness (and hence increasing trust) or decrease the untrustworthiness (and hence decreasing distrust) of their AI systems.
Different factors distinctly contribute to the increase and decrease of trust, as opposed to those factors affecting distrust lewicki1998trust.
This has been empirically demonstrated in other areas of human-computer interaction, where varying website characteristics distinctly contributed to trust and distrust seckler2015trust.
Similarly, in the context of AI and XAI, different trustworthiness cues may enhance trust (e.g., post-hoc explanations), while other cues could mitigate distrust (e.g., certification labels), and we encourage future research to investigate these potential factors.

However, only with appropriate questionnaires that measure both trust and distrust can such a two-dimensional consideration be done justice.
Existing questionnaires like the TAI and TPA have limitations; the former does not account for distrust, and the latter holds room for improvement to accurately measure both dimensions, as indicated by our results.
Good questionnaire development is rooted in a thorough understanding of the constructs being measured, usually grounded in theory and empirical research aeschbach2021transparency. In light of these requirements, comparatively little effort seems to have been made to understand and measure distrust in AI ueno2022scoping, scharowski2023distrust.
Instead of adopting trust questionnaires from other research areas, we encourage the human-AI and XAI community to consider developing their own trust questionnaires, which take into account the unique nature of human-AI interaction.
This involves generating items for the AI context that capture both trust and distrust.
Not only would such a two-dimensional conceptualization provide the added value outlined above, but also contribute to a more comprehensive and holistic understanding of trust and distrust.

§ LIMITATIONS AND FUTURE WORK

First, the present work utilized crowd-sourcing for participant recruitment. While crowd-sourced data have been shown to be at least as reliable as other, more traditional ways of recruitment, such as student sampling buhrmester2011amazon,douglas2023data, future work should examine how the two trust scales perform across varying populations.

Second, ratings were collected in an online experiment with a scenario-based approach where participants observed AI interactions. While this is a common approach [e.g.,][]holthausen2020sts_ad, schaefer2016measuring that had the advantage of reaching the necessary number of participants for a high-powered validation study, future work should investigate alternative approaches, using other forms of interaction with AI.

Third, the present findings are limited to the context of automated vehicles and chatbots. While these are arguably timely and crucial application areas of AI and our findings are largely consistent with prior work in automation spain2008towards and preliminary findings for the AI domain perrig2023trust, future work should consider additional AI contexts, such as medical diagnosis or content recommendations.

Finally, a general limitation of statistical factor analysis is that the item wording, particularly the simultaneous use of positively and negatively formulated items, potentially influences participant responses perrig2023pos, sauro2011positive. Negatively formulated items can lead participants to intentionally or unintentionally ignore or misunderstand these items.  The resulting response patterns may load on two distinct factors in a factor analysis due to methodological issues related to the item wording lewis2017revisiting. Such methodological issues could be an alternative explanation for the revealed two-factor structure, and we recognize these challenges. Distorted factor structures have been shown for scales of usability lewis2017revisiting, lewis2013umux and website aesthetics perrig2023pos, where an argument was made to not distinguish factors based on item wording because it lacked theoretical ground. In the case of trust, however, we pointed out that a distinction between trust and distrust is theoretically justified and has merits that go beyond positive or negative item formulation peters2023importance, scharowski2023distrust. Ultimately, the underlying structure of psychological constructs, such as trust, is not rooted in statistical but rather in theoretical considerations fried2020theories. We want to emphasize that the psychometric validation of the TPA and TAI, along with our recommendations for using these two scales, remain robust despite this limitation. While our work thus contributes to more reliable and valid tools for measuring trust, it should not be taken as the final verdict in the discourse regarding the dimensionality of trust and distrust, calling for further work.

§ CONCLUSION

Trust is a central and frequently measured construct in studying human-AI interactions. However, no validated trust questionnaire explicitly designed for the context of AI exists to date, with researchers relying on scales developed for other research areas, such as automation or human-human interaction. Motivated by the need for validated and standardized questionnaires, the present work reported on the first comprehensive validation of two trust scales in the context of AI, the popular TPA jian2000foundations and the recently published TAI hoffman2023measures. In a 2x2 online study design, using two conditions (trustworthy vs. untrustworthy) and two areas of applications (AV vs. chatbot), 2970 complete responses to the two scales and related measures were collected from 1485 participants.

While results from the psychometric evaluation supported both scales' psychometric quality regarding reliability, convergent, divergent, and criterion validity, findings were less favorable concerning the TPA's construct validity. Consequently, we investigated ways to improve the TPA, namely item removal and an alternative two-factor model, which enhanced the scale's psychometric quality. From our findings, we derived recommendations for researchers and practitioners who want to use the TPA and TAI in the context of AI. Results emphasized that while the TAI only measures trust, the TPA can measure two constructs, trust and distrust. Based on these findings, we highlighted the practical and theoretical implications of accounting for both trust and distrust, underscoring the added value of this distinction beyond a theoretical discussion to actual measurement practice. However, the TPA and TAI are not optimized for measuring both trust and distrust in the AI context, at least in their current versions. We therefore encourage future work on the TPA or the development of a scale explicitly designed for the context of AI, which measures both constructs. Such a distinction could contribute to a deeper and more nuanced understanding of trust and distrust in the human-AI interaction in a world where AI increasingly has the potential for both benefits and harm.

§ DATA AVAILABILITY STATEMENT

This research was financed entirely by our research group; we received no additional funding. The pre-registration (<https://doi.org/10.17605/OSF.IO/3EU4V>) and supplementary materials (<https://doi.org//10.17605/OSF.IO/7CDNE>) for this study are available on OSF.

§ FUNDING AND DECLARATION OF CONFLICTING INTERESTS

This work is financed entirely by the corresponding author's research group, as we received no additional funding. The authors have no commercial or financial relationships to declare that could be construed as a potential conflict of interest.

Missing 'biblatex' package
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New York, NY, USA

Association for Computing Machinery

journaltitleProc. ACM Hum.-Comput. Interact.
numberCHI PLAY
titleTransparency in Measurement Reporting: A Systematic Literature Review of CHI PLAY
methodology,measurement,open science,transparency,survey,validity,literature review

journaltitleIEEE Technology and Society Magazine
titleTrusting intelligent machines: Deepening trust within socio-technical systems

journaltitleProceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)
titleAre trust and distrust distinct constructs? An empirical study of the effects of trust and distrust among online banking users

journaltitlearXiv Preprint arXiv:2204.13480
noteRetrieved from <https://arxiv.org/abs/2204.13480>
titleThe Value of Measuring Trust in AI-A Socio-Technical System Perspective

journaltitleGames and economic behavior
titleTrust, reciprocity, and social history
family=van Kleek,

New York, NY

booktitleProceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18
title'It's Reducing a Human Being to a Percentage'
Nicolas Scharowski
Citavi 6
Citavi Attachments
Authors' choice;by semicolons;include commas;of terms;required.;separated;within terms only

journaltitleMethods in Psychology
titleThe Quality of Data Collected Online: An Investigation of Careless Responding in a Crowdsourced Sample

Cagliari, Italy

abstractExplainable artificially intelligent (XAI) systems form part of sociotechnical systems, e.g., human+AI teams tasked with making decisions. Yet, current XAI systems are rarely evaluated by measuring the performance of human+AI teams on actual decision-making tasks. We conducted two online experiments and one in-person think-aloud study to evaluate two currently common techniques for evaluating XAI systems: (1) using proxy, artificial tasks such as how well humans predict the AI's decision from the given explanations, and (2) using subjective measures of trust and preference as predictors of actual performance. The results of our experiments demonstrate that evaluations with proxy tasks did not predict the results of the evaluations with the actual decision-making tasks. Further, the subjective measures on evaluations with actual decision-making tasks did not predict the objective performance on those same tasks. Our results suggest that by employing misleading evaluation methods, our field may be inadvertently slowing its progress toward developing human+AI teams that can reliably perform better than humans or AIs alone.
journaltitleProceedings of the 25th International Conference on Intelligent User Interfaces
seriesIUI '20
titleProxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
artificial intelligence,explanations,trust

journaltitlePerspectives on Psychological Science
titleAmazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?

American Psychological Association

journaltitlePsychological Bulletin
titleRelationship Between Attitudes and Evaluative Space: A Critical Review, with Emphasis on the Separability of Positive and Negative Substrates.

Hoboken, NJ

John Wiley & Sons

titleTrust theory: A socio-cognitive and computational model

Journal of Electronic Commerce Research

journaltitleJournal of Electronic Commerce Research
titleAntecedents and distinctions between online trust and distrust: Predicting high-and low-risk internet behaviors

titleCoefficient alpha and the internal structure of tests

journaltitleJournal of Experimental Social Psychology
titleMethods for the detection of carelessly invalid responses in survey data

Management Information Systems Research Center, University of Minnesota

journaltitleMIS Quarterly
titlePerceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology

Thousand Oaks, CA

SAGE publications, Inc.

titleScale Development: Theory and Applications

Virtual/New Orleans, LA, USA

Association for Computing Machinery

journaltitleCHI 2022 TRAIT Workshop on Trust and Reliance in AI-Human Teams
titleTowards a Generalized Scale to Measure Situational Trust in AI Systems

journaltitlePLoS ONE
titleData quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA

journaltitleBritish Journal of Psychology
titleFrom alpha to omega: A practical solution to the pervasive problem of internal consistency estimation

journaltitleIEEE Transactions on Human-Machine Systems
titleCreating appropriate trust in automated vehicle systems: A framework for HMI design

journaltitleComputers in Human Behavior
titleThree Strikes and you are out!: The impacts of multiple human–robot trust violations and repairs on robot trustworthiness
Human–robot interaction,HRI,Trust repair,Trustworthiness,Error recovery,Ability,Benevolence,Integrity

journaltitleAdvances in Methods and Practices in Psychological Science
titleMeasurement Schmeasurement: Questionable Measurement Practices and How to Avoid Them

journaltitlePsychological Inquiry
titleTheories and Models: What They Are, What They Are for, and What They Are About

New York, NY

Free Press

titleTrust: The social virtues and the creation of prosperity

SAGE publications, Ltd.

titleScale Construction and Psychometrics for Social and Personality Psychology

Department of Sociology, University of Oxford

booktitleTrust: Making and Breaking Cooperative Relations, electronic edition
titleCan We Trust Trust?

New York, NY

titleIBM SPSS statistics 26 step by step: A simple guide and reference

Hoboken, NJ

Prentice Hall

titleMultivariate Data Analysis

Boston, MA

AMCIS 2001 Proceedings

journaltitle7th Americas Conference on Information Systems
titleWhile trust is cool and collected, distrust is fiery and frenzied: A model of distrust concepts

Taylor & Francis

journaltitleCommunications in Statistics-Theory and Methods
titleA Class of Invariant Consistent Tests for Multivariate Normality

Los Angeles, CA, USA

Sage Publications

journaltitleHuman Factors
titleTrust in Automation: Integrating Empirical Evidence on Factors That Influence Trust

journaltitleIEEE Intelligent Systems
titleThe dynamics of trust in cyberdomains

journaltitleFrontiers in Computer Science
titleMeasures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance

Virtual Event, DC, USA

Association for Computing Machinery

journaltitle12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
seriesAutomotiveUI '20
titleSituational Trust Scale for Automated Driving (STS-AD): Development and Initial Validation

titleEducational and Psychological Measurement and Evaluation

Taylor     Francis

journaltitleInternational Journal of Human–Computer Interaction
titleA Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve?

journaltitleStructural Equation Modeling: A Multidisciplinary Journal
titleCutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus new Alternatives

Virtual Event, Canada

abstractTrust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the cognitive mechanism of trust, and how can we promote them, or assess whether they are being satisfied in a given interaction? This work aims to answer these questions. We discuss a model of trust inspired by, but not identical to, interpersonal trust (i.e., trust between people) as defined by sociologists. This model rests on two key properties: the vulnerability of the user; and the ability to anticipate the impact of the AI model's decisions. We incorporate a formalization of 'contractual trust', such that trust between a user and an AI model is trust that some implicit or explicit contract will hold, and a formalization of 'trustworthiness' (that detaches from the notion of trustworthiness in sociology), and with it concepts of 'warranted' and 'unwarranted' trust. We present the possible causes of warranted trust as intrinsic reasoning and extrinsic behavior, and discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted. Finally, we elucidate the connection between trust and XAI using our formalization.
journaltitleProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '21
titleFormalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
distrust,trustworthy,sociology,formalization,warranted trust,artificial intelligence,trust,contractual trust

Seoul, Republic of Korea

Association for Computing Machinery

abstractPrivate companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible artificial intelligence technologies. While their recommendations converge on a set of central values, little is known about the values a more representative public would find important for the AI technologies they interact with and might be affected by. We conducted a survey examining how individuals perceive and prioritize responsible AI values across three groups: a representative sample of the US population (N=743), a sample of crowdworkers (N=755), and a sample of AI practitioners (N=175). Our results empirically confirm a common concern: AI practitioners’ value priorities differ from those of the general public. Compared to the US-representative sample, AI practitioners appear to consider responsible AI values as less important and emphasize a different set of values. In contrast, self-identified women and black respondents found responsible AI values more important than other groups. Surprisingly, more liberal-leaning participants, rather than participants reporting experiences with discrimination, were more likely to prioritize fairness than other groups. Our findings highlight the importance of paying attention to who gets to define “responsible AI.”
journaltitle2022 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '22
titleHow Different Groups Prioritize Ethical Values for Responsible AI
empirical ethics,Responsible AI,value-sensitive design

London, UK, GBR

journaltitleInternational Journal of Cognitive Ergonomics
titleFoundations for an Empirically Determined Scale of Trust in Automated Systems

European Respiratory Society

journaltitleEuropean Respiratory Journal
titleValidated Questionnaires Should not be Modified

New Orleans, LA, USA

Association for Computing Machinery

abstractMost prior work on human-AI interaction is set in communities that indicate skepticism towards AI, but we know less about contexts where AI is viewed as aspirational. We investigated the perceptions around AI systems by drawing upon 32 interviews and 459 survey respondents in India. Not only do Indian users accept AI decisions (79.2% respondents indicate acceptance), we find a case of AI authority—AI has a legitimized power to influence human actions, without requiring adequate evidence about the capabilities of the system. AI authority manifested into four user attitudes of vulnerability: faith, forgiveness, self-blame, and gratitude, pointing to higher tolerance for system misfires, and introducing potential for irreversible individual and societal harm. We urgently call for calibrating AI authority, reconsidering success metrics and responsible AI approaches and present methodological suggestions for research and deployments in India.
journaltitleProceedings of the 2022 CHI Conference on Human Factors in Computing Systems
seriesCHI ’22
title“Because AI is 100% Right and Safe’’: User Attitudes and Sources of AI Authority in India
algorithmic decision-making,artificial intelligence,perceptions of AI,India

SAGE Publications Sage CA: Los Angeles, CA

journaltitleHuman factors
titleTrust in artificial intelligence: Meta-analytic findings

New York, NY

The Guilford Press

titlePrinciples and practice of structural equation modeling

Frontiers Media SA

journaltitleFrontiers in Psychology
titleMeasurement of trust in automation: A narrative review and reference guide

Taylor & Francis

journaltitleJournal of Trust Research
titleUnlocking the treasure trove: How can Luhmann’s theory of trust enrich trust research?

Chicago, IL, USA

Association for Computing Machinery

abstractAI systems are adopted in numerous domains due to their increasingly strong predictive performance. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time-consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our work highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other’s work and produce generalizable scientific knowledge. We also hope this work will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.
journaltitleProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '23
titleTowards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies

Amsterdam, NLD

journaltitleArtificial Intelligence
titleWhat Do We Want From Explainable Artificial Intelligence (XAI)? A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research

New Orleans, LA, USA

Association for Computing Machinery

abstractIn the media, in policy-making, but also in research articles, algorithmic decision-making (ADM) systems are referred to as algorithms, artificial intelligence, and computer programs, amongst other terms. We hypothesize that such terminological differences can affect people’s perceptions of properties of ADM systems, people’s evaluations of systems in application contexts, and the replicability of research as findings may be influenced by terminological differences. In two studies (N = 397, N = 622), we show that terminology does indeed affect laypeople’s perceptions of system properties (e.g., perceived complexity) and evaluations of systems (e.g., trust). Our findings highlight the need to be mindful when choosing terms to describe ADM systems, because terminology can have unintended consequences, and may impact the robustness and replicability of HCI research. Additionally, our findings indicate that terminology can be used strategically (e.g., in communication about ADM systems) to influence people’s perceptions and evaluations of these systems.
journaltitleProceedings of the 2022 CHI Conference on Human Factors in Computing Systems
seriesCHI '22
title“Look! It’s a Computer Program! It’s an Algorithm! It’s AI!”: Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?
replicability,terminology,human-centered AI,research methodology

London, UK, GBR

SAGE Publications

journaltitleHuman factors
titleTrust in Automation: Designing for Appropriate Reliance

Academy of Management Briarcliff Manor, NY 10510

journaltitleThe Academy of Management Review
titleTrust and Distrust: New Relationships and Realities

Sage Publications Sage CA: Thousand Oaks, CA

journaltitleJournal of management
titleModels of interpersonal trust development: Theoretical approaches, empirical evidence, and future directions

journaltitleJournal of Usability Studies
titleRevisiting the Factor Structure of the System Usability Scale

Paris, France

Association for Computing Machinery

journaltitleProceedings of the SIGCHI Conference on Human Factors in Computing Systems
seriesCHI '13
titleUMUX-LITE: When There's No Time for the SUS

Seoul, Republic of Korea

Association for Computing Machinery

abstractCurrent literature and public discourse on “trust in AI” are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust. Given that AI systems differ in their level of trustworthiness, two open questions come to the fore: how should AI trustworthiness be responsibly communicated to ensure appropriate and equitable trust judgments by different users, and how can we protect users from deceptive attempts to earn their trust? We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH, which describes how trustworthiness is communicated in AI systems through trustworthiness cues and how those cues are processed by people to make trust judgments. Besides AI-generated content, we highlight transparency and interaction as AI systems’ affordances that present a wide range of trustworthiness cues to users. By bringing to light the variety of users’ cognitive processes to make trust judgments and their potential limitations, we urge technology creators to make conscious decisions in choosing reliable trustworthiness cues for target users and, as an industry, to regulate this space and prevent malicious use. Towards these goals, we define the concepts of warranted trustworthiness cues and expensive trustworthiness cues, and propose a checklist of requirements to help technology creators identify appropriate cues to use. We present a hypothetical use case to illustrate how practitioners can use MATCH to design AI systems responsibly, and discuss future directions for research and industry efforts aimed at promoting responsible trust in AI.
journaltitleProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '22
titleDesigning for Responsible Trust in AI Systems: A Communication Perspective
human-centered AI,human-AI interaction,Trust in AI,AI design

New York, NY, USA

abstractSupervised machine-learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world?
titleThe Mythos of Model Interpretability: In Machine Learning, the Concept of Interpretability is Both Important and Slippery.

MCB UP Ltd

journaltitleJournal of Managerial Psychology
titleA caveat on using single-item versus multiple-item scales

Yokohama, Japan

Association for Computing Machinery

abstractThis paper addresses an under-explored problem of AI-assisted decision-making: when objective performance information of the machine learning model underlying a decision aid is absent or scarce, how do people decide their reliance on the model? Through three randomized experiments, we explore the heuristics people may use to adjust their reliance on machine learning models when performance feedback is limited. We find that the level of agreement between people and a model on decision-making tasks that people have high confidence in significantly affects reliance on the model if people receive no information about the model’s performance, but this impact will change after aggregate-level model performance information becomes available. Furthermore, the influence of high confidence human-model agreement on people’s reliance on a model is moderated by people’s confidence in cases where they disagree with the model. We discuss potential risks of these heuristics, and provide design implications on promoting appropriate reliance on AI.
journaltitleProceedings of the 2021 CHI Conference on Human Factors in Computing Systems
seriesCHI '21
titleHuman Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks
Machine learning,appropriate reliance,human-AI interaction

Hoboken, NJ

titleTrust and Power

American Psychological Association

journaltitlePsychological methods
titlePower analysis and determination of sample size for covariance structure modeling

issn0006-3444, 1464-3510
titleMeasures of Multivariate Skewness and Kurtosis with Applications

Briarcliff Manor, NY, USA

Academy of Management

journaltitleAcademy of Management Review
titleAn Integrative Model of Organizational Trust

New York, NY

Psychology Press

titleTest theory: A unified treatment

Taylor & Francis

journaltitleInternational journal of electronic commerce
titleWhat trust means in e-commerce customer relationships: An interdisciplinary conceptual typology

American Psychological Association

journaltitlePsychological methods
titleIdentifying careless responses in survey data.

Los Angeles, CA, USA

Sage Publications

journaltitleHuman Factors
titleAffective Processes in Human–Automation Interactions

journaltitlearXiv: Human-Computer Interaction
noteRetrieved from <https://arxiv.org/abs/1811.11839>
titleA Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

Berlin, Germany

Springer Berlin, Heidelberg

titleTesttheorie und Fragebogenkonstruktion [Test Theory and Questionnaire Construction]

Taylor & Francis

titleTrust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems

New York, NY, USA

Association for Computing Machinery

journaltitleCommun. ACM
titleTo Trust or to Distrust, That is the Question: Investigating the Trust-Distrust Paradox

Los Angeles, CA, USA

Sage Publications

journaltitleHuman Factors
titleHumans and Automation: Use, Misuse, Disuse, Abuse

journaltitleBehavior Research Methods
titleData quality of platforms and panels for online behavioral research

Hamburg, Germany

Association for Computing Machinery

journaltitleExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
seriesCHI EA '23
titleTrust Issues with Trust Scales: Examining the Psychometric Quality of Trust Measures in the Context of AI

Taylor     Francis

journaltitleInternational Journal of Human–Computer Interaction
titleDevelopment and Validation of a Positive-Item Version of the Visual Aesthetics of Websites Inventory: The VisAWI-Pos

Springer Nature Switzerland

booktitleExplainable Artificial Intelligence
titleThe Importance of Distrust in AI

Yokohama, Japan

abstractWith machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models. Although many supposedly interpretable models have been proposed, there have been relatively few experimental studies investigating whether these models achieve their intended effects, such as making people more closely follow a model’s predictions when it is beneficial for them to do so or enabling them to detect when a model has made a mistake. We present a sequence of pre-registered experiments (N = 3, 800) in which we showed participants functionally identical models that varied only in two factors commonly thought to make machine learning models more or less interpretable: the number of features and the transparency of the model (i.e., whether the model internals are clear or black box). Predictably, participants who saw a clear model with few features could better simulate the model’s predictions. However, we did not find that participants more closely followed its predictions. Furthermore, showing participants a clear model meant that they were less able to detect and correct for the model’s sizable mistakes, seemingly due to information overload. These counterintuitive findings emphasize the importance of testing over intuition when developing interpretable models.
journaltitleProceedings of the 2021 CHI Conference on Human Factors in Computing Systems
seriesCHI '21
titleManipulating and Measuring Model Interpretability
human-centered machine learning,machine-assisted decision making,interpretability
family=R Core Team,

Vienna, Austria

R Foundation for Statistical Computing

titleR: A Language and Environment for Statistical Computing

Briarcliff Manor, NY, USA

Academy of Management Briarcliff Manor, NY 10510

journaltitleAcademy of Management Review
titleNot So Different After All: A Cross-Discipline View Of Trust

Sage Publications Sage UK: London, England

journaltitleHuman Relations
titleTrust and distrust: Polar opposites, or independent but co-existing?

Vancouver, BC, Canada

Association for Computing Machinery

journaltitleProceedings of the SIGCHI Conference on Human Factors in Computing Systems
seriesCHI '11
titleWhen Designing Usability Questionnaires, Does It Hurt to Be Positive?

Boston, MA

Springer US

booktitleRobust Intelligence and Trust in Autonomous Systems
titleMeasuring Trust in Human Robot Interactions: Development of the "Trust Perception Scale-HRI"

Hamburg, Germany

Association for Computing Machinery

journaltitleCHI 2023 TRAIT Workshop on Trust and Reliance in AI-Human Teams
titleDistrust in (X)AI – Measurement Artifact or Distinct Construct?

Virtual/New Orleans, LA, USA

Association for Computing Machinery

journaltitleCHI TRAIT Workshop
titleTrust and Reliance in XAI–Distinguishing Between Attitudinal and Behavioral Measures

Chicago, IL, USA

Association for Computing Machinery

abstractAuditing plays a pivotal role in the development of trustworthy AI. However, current research primarily focuses on creating auditable AI documentation, which is intended for regulators and experts rather than end-users affected by AI decisions. How to communicate to members of the public that an AI has been audited and considered trustworthy remains an open challenge. This study empirically investigated certification labels as a promising solution. Through interviews (N = 12) and a census-representative survey (N = 302), we investigated end-users’ attitudes toward certification labels and their effectiveness in communicating trustworthiness in low- and high-stakes AI scenarios. Based on the survey results, we demonstrate that labels can significantly increase end-users’ trust and willingness to use AI in both low- and high-stakes scenarios. However, end-users’ preferences for certification labels and their effect on trust and willingness to use AI were more pronounced in high-stake scenarios. Qualitative content analysis of the interviews revealed opportunities and limitations of certification labels, as well as facilitators and inhibitors for the effective use of labels in the context of AI. For example, while certification labels can mitigate data-related concerns expressed by end-users (e.g., privacy and data protection), other concerns (e.g., model performance) are more challenging to address. Our study provides valuable insights and recommendations for designing and implementing certification labels as a promising constituent within the trustworthy AI ecosystem.
journaltitleProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '23
titleCertification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study
Seal,User study,Certification,Trust,Documentation,AI,Label,Trustworthy,Audit

titleA Micro and Macro Perspective on Trustworthiness: Theoretical Underpinnings of the Trustworthiness Assessment Model (TrAM)

journaltitleComputers in human behavior
titleTrust and distrust on the web: User experiences and website characteristics

journaltitleOrganization science
titleExplaining the limited effectiveness of legalistic “remedies” for trust/distrust

Los Angeles, USA

SAGE Publications

journaltitleProceedings of the Human Factors and Ergonomics Society Annual Meeting
titleTowards an Empirically Developed Scale for System Trust: Take Two

Virtual Event, Canada

Association for Computing Machinery

abstractEnvironmental data science is uniquely placed to respond to essentially complex and fantastically worthy challenges related to arresting planetary destruction. Trust is needed for facilitating collaboration between scientists who may share datasets and algorithms, and for crafting appropriate science-based policies. Achieving this trust is particularly challenging because of the numerous complexities, multi-scale variables, interdependencies and multi-level uncertainties inherent in environmental data science. Virtual Labs—easily accessible online environments provisioning access to datasets, analysis and visualisations—are socio-technical systems which, if carefully designed, might address these challenges and promote trust in a variety of ways. In addition to various system properties that can be utilised in support of effective collaboration, certain features which are commonly seen to benefit trust—transparency and provenance in particular—appear applicable to promoting trust in and through Virtual Labs. Attempting to realise these features in their design reveals, however, that their implementation is more nuanced and complex than it would appear. Using the lens of affordances, we argue for the need to carefully articulate these features, with consideration of multiple stakeholder needs on balance, so that these Virtual Labs do in fact promote trust. We argue that these features not be conceived as widgets that can be imported into a given context to promote trust; rather, whether they promote trust is a function of how systematically designers consider various (potentially conflicting) stakeholder trust needs.
journaltitleProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '21
titleFifty Shades of Grey: In Praise of a Nuanced Approach Towards Trustworthy Design
Environmental Data Science,Trust,Affordances,Virtual Research Environments

New York, NY

journaltitleProceedings of the 2020 Conference on Fairness, Accountability, and Transparency
titleThe Relationship Between Trust in AI and Trustworthy Machine Learning Technologies

New Orleans, LA, USA

Association for Computing Machinery

journaltitleExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
seriesCHI EA '22
titleTrust in Human-AI Interaction: Scoping Out Models, Measures, and Methods
Decision aids,Scoping review,Robots,Automation,Trust,Artificial intelligence

New York, NY, USA

Association for Computing Machinery

journaltitleProc. ACM Hum.-Comput. Interact.
titleHow to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies
methodology,decision making,trust,artificial intelligence

journaltitleFuzzy Sets and Systems
titleGradual trust and distrust in recommender systems

American Psychological Association

journaltitleJournal of personality and social psychology
titleDevelopment and validation of brief measures of positive and negative affect: the PANAS scales.

journaltitleJournal on Multimodal User Interfaces
title“Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design

New York, NY

titleA beginner's guide to structural equation modeling

Hamburg, Germany

Association for Computing Machinery

journaltitleProceedings of the 2023 CHI Conference on Human Factors in Computing Systems
seriesCHI '23
titleMeasuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions
automation,warranted trust,trust calibration,survey,empirical studies

Glasgow, Scotland Uk

Association for Computing Machinery

journaltitleProceedings of the 2019 CHI Conference on Human Factors in Computing Systems
seriesCHI '19
titleUnderstanding The Effect of Accuracy on Trust in Machine Learning Models

Limassol, Cyprus

Association for Computing Machinery

journaltitleProceedings of the 22nd international conference on intelligent user interfaces
seriesIUI '17
titleUser Trust Dynamics: An Investigation Driven by Differences in System Performance

New York, NY, USA

Association for Computing Machinery

journaltitleProc. ACM Hum.-Comput. Interact.
numberCHI PLAY
titleTransparency in Measurement Reporting: A Systematic Literature Review of CHI PLAY
methodology,measurement,open science,transparency,survey,validity,literature review

journaltitleIEEE Technology and Society Magazine
titleTrusting intelligent machines: Deepening trust within socio-technical systems

journaltitleProceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)
titleAre trust and distrust distinct constructs? An empirical study of the effects of trust and distrust among online banking users

journaltitlearXiv Preprint arXiv:2204.13480
noteRetrieved from <https://arxiv.org/abs/2204.13480>
titleThe Value of Measuring Trust in AI-A Socio-Technical System Perspective

journaltitleGames and economic behavior
titleTrust, reciprocity, and social history
family=van Kleek,

New York, NY

booktitleProceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18
title'It's Reducing a Human Being to a Percentage'
Nicolas Scharowski
Citavi 6
Citavi Attachments
Authors' choice;by semicolons;include commas;of terms;required.;separated;within terms only

journaltitleMethods in Psychology
titleThe Quality of Data Collected Online: An Investigation of Careless Responding in a Crowdsourced Sample

Cagliari, Italy

abstractExplainable artificially intelligent (XAI) systems form part of sociotechnical systems, e.g., human+AI teams tasked with making decisions. Yet, current XAI systems are rarely evaluated by measuring the performance of human+AI teams on actual decision-making tasks. We conducted two online experiments and one in-person think-aloud study to evaluate two currently common techniques for evaluating XAI systems: (1) using proxy, artificial tasks such as how well humans predict the AI's decision from the given explanations, and (2) using subjective measures of trust and preference as predictors of actual performance. The results of our experiments demonstrate that evaluations with proxy tasks did not predict the results of the evaluations with the actual decision-making tasks. Further, the subjective measures on evaluations with actual decision-making tasks did not predict the objective performance on those same tasks. Our results suggest that by employing misleading evaluation methods, our field may be inadvertently slowing its progress toward developing human+AI teams that can reliably perform better than humans or AIs alone.
journaltitleProceedings of the 25th International Conference on Intelligent User Interfaces
seriesIUI '20
titleProxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
artificial intelligence,explanations,trust

journaltitlePerspectives on Psychological Science
titleAmazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?

American Psychological Association

journaltitlePsychological Bulletin
titleRelationship Between Attitudes and Evaluative Space: A Critical Review, with Emphasis on the Separability of Positive and Negative Substrates.

Hoboken, NJ

John Wiley & Sons

titleTrust theory: A socio-cognitive and computational model

Journal of Electronic Commerce Research

journaltitleJournal of Electronic Commerce Research
titleAntecedents and distinctions between online trust and distrust: Predicting high-and low-risk internet behaviors

titleCoefficient alpha and the internal structure of tests

journaltitleJournal of Experimental Social Psychology
titleMethods for the detection of carelessly invalid responses in survey data

Management Information Systems Research Center, University of Minnesota

journaltitleMIS Quarterly
titlePerceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology

Thousand Oaks, CA

SAGE publications, Inc.

titleScale Development: Theory and Applications

Virtual/New Orleans, LA, USA

Association for Computing Machinery

journaltitleCHI 2022 TRAIT Workshop on Trust and Reliance in AI-Human Teams
titleTowards a Generalized Scale to Measure Situational Trust in AI Systems

journaltitlePLoS ONE
titleData quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA

journaltitleBritish Journal of Psychology
titleFrom alpha to omega: A practical solution to the pervasive problem of internal consistency estimation

journaltitleIEEE Transactions on Human-Machine Systems
titleCreating appropriate trust in automated vehicle systems: A framework for HMI design

journaltitleComputers in Human Behavior
titleThree Strikes and you are out!: The impacts of multiple human–robot trust violations and repairs on robot trustworthiness
Human–robot interaction,HRI,Trust repair,Trustworthiness,Error recovery,Ability,Benevolence,Integrity

journaltitleAdvances in Methods and Practices in Psychological Science
titleMeasurement Schmeasurement: Questionable Measurement Practices and How to Avoid Them

journaltitlePsychological Inquiry
titleTheories and Models: What They Are, What They Are for, and What They Are About

New York, NY

Free Press

titleTrust: The social virtues and the creation of prosperity

SAGE publications, Ltd.

titleScale Construction and Psychometrics for Social and Personality Psychology

Department of Sociology, University of Oxford

booktitleTrust: Making and Breaking Cooperative Relations, electronic edition
titleCan We Trust Trust?

New York, NY

titleIBM SPSS statistics 26 step by step: A simple guide and reference

Hoboken, NJ

Prentice Hall

titleMultivariate Data Analysis

Boston, MA

AMCIS 2001 Proceedings

journaltitle7th Americas Conference on Information Systems
titleWhile trust is cool and collected, distrust is fiery and frenzied: A model of distrust concepts

Taylor & Francis

journaltitleCommunications in Statistics-Theory and Methods
titleA Class of Invariant Consistent Tests for Multivariate Normality

Los Angeles, CA, USA

Sage Publications

journaltitleHuman Factors
titleTrust in Automation: Integrating Empirical Evidence on Factors That Influence Trust

journaltitleIEEE Intelligent Systems
titleThe dynamics of trust in cyberdomains

journaltitleFrontiers in Computer Science
titleMeasures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance

Virtual Event, DC, USA

Association for Computing Machinery

journaltitle12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
seriesAutomotiveUI '20
titleSituational Trust Scale for Automated Driving (STS-AD): Development and Initial Validation

titleEducational and Psychological Measurement and Evaluation

Taylor     Francis

journaltitleInternational Journal of Human–Computer Interaction
titleA Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve?

journaltitleStructural Equation Modeling: A Multidisciplinary Journal
titleCutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus new Alternatives

Virtual Event, Canada

abstractTrust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the cognitive mechanism of trust, and how can we promote them, or assess whether they are being satisfied in a given interaction? This work aims to answer these questions. We discuss a model of trust inspired by, but not identical to, interpersonal trust (i.e., trust between people) as defined by sociologists. This model rests on two key properties: the vulnerability of the user; and the ability to anticipate the impact of the AI model's decisions. We incorporate a formalization of 'contractual trust', such that trust between a user and an AI model is trust that some implicit or explicit contract will hold, and a formalization of 'trustworthiness' (that detaches from the notion of trustworthiness in sociology), and with it concepts of 'warranted' and 'unwarranted' trust. We present the possible causes of warranted trust as intrinsic reasoning and extrinsic behavior, and discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted. Finally, we elucidate the connection between trust and XAI using our formalization.
journaltitleProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '21
titleFormalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
distrust,trustworthy,sociology,formalization,warranted trust,artificial intelligence,trust,contractual trust

Seoul, Republic of Korea

Association for Computing Machinery

abstractPrivate companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible artificial intelligence technologies. While their recommendations converge on a set of central values, little is known about the values a more representative public would find important for the AI technologies they interact with and might be affected by. We conducted a survey examining how individuals perceive and prioritize responsible AI values across three groups: a representative sample of the US population (N=743), a sample of crowdworkers (N=755), and a sample of AI practitioners (N=175). Our results empirically confirm a common concern: AI practitioners’ value priorities differ from those of the general public. Compared to the US-representative sample, AI practitioners appear to consider responsible AI values as less important and emphasize a different set of values. In contrast, self-identified women and black respondents found responsible AI values more important than other groups. Surprisingly, more liberal-leaning participants, rather than participants reporting experiences with discrimination, were more likely to prioritize fairness than other groups. Our findings highlight the importance of paying attention to who gets to define “responsible AI.”
journaltitle2022 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '22
titleHow Different Groups Prioritize Ethical Values for Responsible AI
empirical ethics,Responsible AI,value-sensitive design

London, UK, GBR

journaltitleInternational Journal of Cognitive Ergonomics
titleFoundations for an Empirically Determined Scale of Trust in Automated Systems

European Respiratory Society

journaltitleEuropean Respiratory Journal
titleValidated Questionnaires Should not be Modified

New Orleans, LA, USA

Association for Computing Machinery

abstractMost prior work on human-AI interaction is set in communities that indicate skepticism towards AI, but we know less about contexts where AI is viewed as aspirational. We investigated the perceptions around AI systems by drawing upon 32 interviews and 459 survey respondents in India. Not only do Indian users accept AI decisions (79.2% respondents indicate acceptance), we find a case of AI authority—AI has a legitimized power to influence human actions, without requiring adequate evidence about the capabilities of the system. AI authority manifested into four user attitudes of vulnerability: faith, forgiveness, self-blame, and gratitude, pointing to higher tolerance for system misfires, and introducing potential for irreversible individual and societal harm. We urgently call for calibrating AI authority, reconsidering success metrics and responsible AI approaches and present methodological suggestions for research and deployments in India.
journaltitleProceedings of the 2022 CHI Conference on Human Factors in Computing Systems
seriesCHI ’22
title“Because AI is 100% Right and Safe’’: User Attitudes and Sources of AI Authority in India
algorithmic decision-making,artificial intelligence,perceptions of AI,India

SAGE Publications Sage CA: Los Angeles, CA

journaltitleHuman factors
titleTrust in artificial intelligence: Meta-analytic findings

New York, NY

The Guilford Press

titlePrinciples and practice of structural equation modeling

Frontiers Media SA

journaltitleFrontiers in Psychology
titleMeasurement of trust in automation: A narrative review and reference guide

Taylor & Francis

journaltitleJournal of Trust Research
titleUnlocking the treasure trove: How can Luhmann’s theory of trust enrich trust research?

Chicago, IL, USA

Association for Computing Machinery

abstractAI systems are adopted in numerous domains due to their increasingly strong predictive performance. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time-consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our work highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other’s work and produce generalizable scientific knowledge. We also hope this work will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.
journaltitleProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '23
titleTowards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies

New Orleans, LA, USA

Association for Computing Machinery

abstractIn the media, in policy-making, but also in research articles, algorithmic decision-making (ADM) systems are referred to as algorithms, artificial intelligence, and computer programs, amongst other terms. We hypothesize that such terminological differences can affect people’s perceptions of properties of ADM systems, people’s evaluations of systems in application contexts, and the replicability of research as findings may be influenced by terminological differences. In two studies (N = 397, N = 622), we show that terminology does indeed affect laypeople’s perceptions of system properties (e.g., perceived complexity) and evaluations of systems (e.g., trust). Our findings highlight the need to be mindful when choosing terms to describe ADM systems, because terminology can have unintended consequences, and may impact the robustness and replicability of HCI research. Additionally, our findings indicate that terminology can be used strategically (e.g., in communication about ADM systems) to influence people’s perceptions and evaluations of these systems.
journaltitleProceedings of the 2022 CHI Conference on Human Factors in Computing Systems
seriesCHI '22
title“Look! It’s a Computer Program! It’s an Algorithm! It’s AI!”: Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?
replicability,terminology,human-centered AI,research methodology

Amsterdam, NLD

journaltitleArtificial Intelligence
titleWhat Do We Want From Explainable Artificial Intelligence (XAI)? A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research

London, UK, GBR

SAGE Publications

journaltitleHuman factors
titleTrust in Automation: Designing for Appropriate Reliance

Academy of Management Briarcliff Manor, NY 10510

journaltitleThe Academy of Management Review
titleTrust and Distrust: New Relationships and Realities

Sage Publications Sage CA: Thousand Oaks, CA

journaltitleJournal of management
titleModels of interpersonal trust development: Theoretical approaches, empirical evidence, and future directions

journaltitleJournal of Usability Studies
titleRevisiting the Factor Structure of the System Usability Scale

Paris, France

Association for Computing Machinery

journaltitleProceedings of the SIGCHI Conference on Human Factors in Computing Systems
seriesCHI '13
titleUMUX-LITE: When There's No Time for the SUS

Seoul, Republic of Korea

Association for Computing Machinery

abstractCurrent literature and public discourse on “trust in AI” are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust. Given that AI systems differ in their level of trustworthiness, two open questions come to the fore: how should AI trustworthiness be responsibly communicated to ensure appropriate and equitable trust judgments by different users, and how can we protect users from deceptive attempts to earn their trust? We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH, which describes how trustworthiness is communicated in AI systems through trustworthiness cues and how those cues are processed by people to make trust judgments. Besides AI-generated content, we highlight transparency and interaction as AI systems’ affordances that present a wide range of trustworthiness cues to users. By bringing to light the variety of users’ cognitive processes to make trust judgments and their potential limitations, we urge technology creators to make conscious decisions in choosing reliable trustworthiness cues for target users and, as an industry, to regulate this space and prevent malicious use. Towards these goals, we define the concepts of warranted trustworthiness cues and expensive trustworthiness cues, and propose a checklist of requirements to help technology creators identify appropriate cues to use. We present a hypothetical use case to illustrate how practitioners can use MATCH to design AI systems responsibly, and discuss future directions for research and industry efforts aimed at promoting responsible trust in AI.
journaltitleProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
seriesFAccT '22
titleDesigning for Responsible Trust in AI Systems: A Communication Perspective
human-centered AI,human-AI interaction,Trust in AI,AI design

New York, NY, USA

abstractSupervised machine-learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world?
titleThe Mythos of Model Interpretability: In Machine Learning, the Concept of Interpretability is Both Important and Slippery.

MCB UP Ltd

journaltitleJournal of Managerial Psychology
titleA caveat on using single-item versus multiple-item scales

Yokohama, Japan

Association for Computing Machinery

abstractThis paper addresses an under-explored problem of AI-assisted decision-making: when objective performance information of the machine learning model underlying a decision aid is absent or scarce, how do people decide their reliance on the model? Through three randomized experiments, we explore the heuristics people may use to adjust their reliance on machine learning models when performance feedback is limited. We find that the level of agreement between people and a model on decision-making tasks that people have high confidence in significantly affects reliance on the model if people receive no information about the model’s performance, but this impact will change after aggregate-level model performance information becomes available. Furthermore, the influence of high confidence human-model agreement on people’s reliance on a model is moderated by people’s confidence in cases where they disagree with the model. We discuss potential risks of these heuristics, and provide design implications on promoting appropriate reliance on AI.