# Towards a Participatory and Social Justice-Oriented Measure of Human-Robot
Trust

Raj Korpan<EMAIL_ADDRESS>0000-0003-0431-9134 Hunter College,
City University of New York695 Park AveNew YorkNew YorkUSA10065

(2024)

###### Abstract.

Many measures of human-robot trust have proliferated across the HRI research
literature because each attempts to capture the factors that impact trust
despite its many dimensions. None of the previous trust measures, however,
address the systems of inequity and structures of power present in HRI
research or attempt to counteract the systematic biases and potential harms
caused by HRI systems. This position paper proposes a participatory and social
justice-oriented approach for the design and evaluation of a trust measure.
This proposed process would iteratively co-design the trust measure with the
community for whom the HRI system is being created. The process would
prioritize that community’s needs and unique circumstances to produce a trust
measure that accurately reflects the factors that impact their trust in a
robot.

human-robot trust, trust measure, participatory design, social justice

††copyright: rightsretained††doi: ††conference: Workshop on Trust in HRI;
March 11, 2024; Boulder, CO††isbn: ††ccs: Computer systems organization
Robotics††ccs: Human- centered computing Participatory design††ccs: Human-
centered computing Collaborative and social computing design and evaluation
methods††ccs: Human- centered computing HCI design and evaluation
methods††ccs: Social and professional topics User characteristics

##  1\. Introduction

In many Human-Robot Interaction (HRI) studies the most important evaluation
metric is often trust in the robot (Law and Scheutz, 2021) because it is
believed that trust is the channel through which robots will be accepted and
successfully deployed in real-world contexts (Kok and Soh, 2020). A human’s
trust in a robot, however, cannot be directly observed (Kok and Soh, 2020). So
there is no objective way to directly measure trust during interactions
(Schaefer, 2013) 111Although some use certain observable behaviors during an
interaction as a proxy objective measure, such as compliance with the robots
suggestions (Salem et al., 2015) or attention focused on the robot as measured
by eye tracking (Jenkins and Jiang, 2010), these behaviors do not directly
observe a human’s mental state (Kohn et al., 2021).. Furthermore, it has been
suggested that trust is multi-faceted (Knowles et al., 2022; Khalid et al.,
2021), dynamic (Kaplan et al., 2021), subjective (Schaefer, 2016), and
affected by the robot’s physical form (Bernotat et al., 2021; Fischer et al.,
2023) and performance (Hancock et al., 2011). These characteristics (lack of
observability and complexity) have resulted in the proliferation of many
different measures of trust in the HRI community, each focused on different
aspects of trust (Chita-Tegmark et al., 2021; Khavas, 2021). This position
paper argues that the development and evaluation of a reliable measure of
human-robot trust should be participatory and justice-oriented.

##  2\. Prior Work in Measure Development

Measures of trust are often a self-report questionnaire that consists of a
series of Likert-style questions that ask about different aspects of trust
(Schaefer, 2016; Baker et al., 2018). The questions attempt to represent a
“universal” experience of trust that applies in all situations and contexts
(e.g., (Muir, 1989; Jian et al., 2000; Madsen and Gregor, 2000)). For example,
the Merritt trust scale asks participants to respond on a 5-point Likert
rating scale to 6 statements, such as “I trust the [technology]” (Merritt,
2011). In contrast, the HRI Trust Scale uses a 7-point Likert rating scale
with 37 statements across 5 attributes: team configuration, team process,
context, task, and system (Yagoda and Gillan, 2012). The Trust Perception
Scale-HRI (TPS-HRI) consists of 40 questions all proceeded by “What percentage
of the time will this robot…” and participants could select between 0% and
100% in increments of 10% (Schaefer, 2016). The Multi-Dimensional Measure of
Trust (MDMT) measures trust with 20 items across five dimensions: reliability,
competency, ethicality, transparency and benevolence (Ullman and Malle, 2019;
Malle and Ullman, 2021).

The differences in these measures demonstrate the disparate ways in which they
have been developed and evaluated. For example, the Merritt trust scale was
created by the author based on their expertise and then only evaluated to show
that trust was related to but distinct from the propensity to trust machines
or like them (Merritt, 2011). This approach is susceptible to encoding the
individual biases of the author. It was also not validated for its
generalizability.

Yagoda and Gillan’s HRI Trust Scale started with a preliminary list of items,
conducted an exploratory study where 11 HRI subject matter experts (SMEs)
provided feedback on this initial list, generated a list of HRI trust scale
items based on the feedback, conducted an online crowdsourced quality
assessment with 100 participants on Amazon Mechanical Turk (MTurk), and then
used factor analysis to determine the final HRI Trust Scale (Yagoda and
Gillan, 2012). Although this approach is an improvement over an individual’s
independent creation of a scale, it still has several limitations. First, the
HRI SMEs used to provide feedback were required to have at least 5 years of
professional experience and have published contributions in the field of HRI
(Yagoda and Gillan, 2012). The authors do not mention any other demographic
information about these experts, so there is no way to know what biases they
may have introduced in their feedback. Second, the use of these experts
reinforces the structures of power in the robotics community (Williams, 2024).
Reliance on these experts maintains the historical inequities of who robotic
systems are built by and for because the robotics community has often excluded
Black people (Howard and Kennedy III, 2020), women (Graesser et al., 2021),
queer people (OrganizersOfQueerin et al., 2023; Korpan, 2023), and other
underrepresented groups (Tanevska et al., 2023).

The TPS-HRI began with an initial list of 156 items identified from a
literature review of over 700 papers, which contained 86 trust scales, and two
initial experiments to identify physical attributes that affect
trustworthiness (Schaefer, 2016). Each item on this initial list was evaluated
on a 7-point Likert rating scale by 159 undergraduate students and statistical
analysis (e.g., principal component analysis) was used to reduce the number of
items to 73 (Schaefer, 2016). The scale was also changed to use percentage
increments at this point so that participants rate items on a range from no
trust (0%) to complete trust (100%) (Schaefer, 2016). Next, 11 SMEs were
surveyed to evaluate each item’s importance to include in a trust scale and
their feedback resulted in the reduction of items to 42 (Schaefer, 2016).
These SMEs were recruited from the United States Army and Air Force Research
Laboratories and university research laboratories and had 4 to 30 years of
experience in robotics research (Schaefer, 2016). Similar to the HRI Trust
Scale, the use of SMEs can be problematic for the same reasons. Finally, two
studies were conducted with undergraduate students to evaluate the validity of
the 42-item scale to see if it was able to capture the dynamics of trust over
time and whether it actually measures trust (Schaefer, 2016). The results of
these final studies eliminated 2 items from the scale and showed that the
final 40 items successfully measured trust across multiple interactions
(Schaefer, 2016).

Although the final TPS-HRI is validated, it mainly relied on convenience
samples of undergraduate students with only two demographic characteristics
reported: binary gender (male or female) and mean age (for only the first
validation study) (Schaefer, 2016). This is likely not a representative sample
for many of the other applications in which this scale may be applied to
measure trust (Baxter et al., 2016). Another issue with the validation studies
was that it was conducted in simulation in the context of a soldier’s
interaction with a robot (Schaefer, 2016). A scale validated to measure trust
in this scenario may not necessarily work in other tasks or physical
interactions.

The MDMT began with a list of 62 words related to trust collected from
dictionaries, related literature, and other trust scales (Malle and Ullman,
2021). An initial MTurk study evaluated whether each term related more to
capacity trust or personal trust, two dimensions of trust identified from the
human-robot trust literature (Malle and Ullman, 2021). Principal component
analysis and clustering were then used to identify a reduced list of 20 items
but split into 4 dimensions (reliable, capable, sincere, and ethical) (Malle
and Ullman, 2021). A second MTurk study then asked participants to sort an
expanded list of 32 items into the 4 dimensions or an “other” category (Malle
and Ullman, 2021). The results were used to select the top three items for
each dimension and a face-valid item was added for each, which resulted in the
initial scale with 16 items that is evaluated on a 7-point Likert rating scale
(Malle and Ullman, 2021). A MTurk study was then used to validate the
sensitivity of each dimension to respond to a change in trust related to that
dimension and the internal consistency of the items in each dimension (Malle
and Ullman, 2021). This validation, however, was done where participants were
presented with a sentence that described a robot’s behavior to evaluate their
initial trust ratings, and then new information would be given and trust
ratings evaluated again (Malle and Ullman, 2021). Similar to the TPS-HRI, this
approach to validation may not result in a trust measure that reliably works
in actual human-robot interactions (simulated or physical).

Subsequent work revised the MDMT to update the dimensions (reliable,
competent, ethical, transparent, and a fifth dimension, benevolence) for a
total of 20 items, again based on an online study where participants had to
sort and group terms, and then validated it in a similar way as the initial
MDMT (Ullman, 2021; Malle and Ullman, 2023). Although an online crowdsourcing
tool may produce a more representative sample than a set of undergraduate
students, other research has investigated ethical concerns regarding the use
of MTurk (Moss et al., 2023), shown significant levels of inattentiveness
among MTurk workers (Saravanos et al., 2021), and explored the many data
quality concerns with MTurk workers (Hauser et al., 2019). Furthermore, the
representativeness of MTurk samples is also questionable – in the MDMT
validation study, for example, 76.5% of the sample self-reported as “Non-
Hispanic White or Euro-American” (Ullman, 2021) which is certainly not
representative of the world population or the United States population
(Bureau, [n. d.]).

The development of both the TPS-HRI and the MDMT took a different approach to
create their initial list of items compared to the Merritt trust scale and the
HRI Trust Scale – they drew items from the existing literature and trust
scales. Although they did not use SMEs to generate this initial list, the
power structures and norms are still reinforced in this approach because the
previous research that was used was developed by the established robotics
community (Williams, 2024; Tanqueray and Larsson, 2023). The next section
proposes an alternative approach for the development and evaluation of a
measure of human-robot trust that incorporates principles of participatory
design and aligns with a social justice-oriented framework for human-robot
interaction.

##  3\. Proposed Approach

Because robots have the opportunity to shape our society, their designers can
reinforce or disrupt systems of inequity (Šabanović, 2010) and force their
cultural norms on the communities where these technologies are deployed
(Hipólito et al., 2023). Beyond interpersonal power, robots can wield
structural, disciplinary, and cultural power that can reinforce the structures
of White Patriarchy (Williams, 2024). Recent work has shown how discrimination
and bias exist at multiple levels in the robotics and artificial intelligence
research communities (Fosch-Villaronga and Poulsen, 2022; Fosch-Villaronga and
Drukarch, 2023). As a result, there have been several calls for a more
equitable HRI, such as the Feminist HRI framework (Winkle et al., 2023b), the
HRI Equitable Design framework (Ostrowski et al., 2022), and the Robots for
Social Justice framework (Zhu et al., 2024). Inspired by these calls, this
position paper suggests a participatory and social justice-oriented human-
robot trust measure.

###  3.1. Participatory Design

Participatory design is a collaborative process that directly involves the
people affected by technology in its creation (Spinuzzi, 2005). Participatory
design approaches in HRI have been used to design systems that meet the needs
of specific users, such as the elderly (Rogers et al., 2022) or people with
physical or mental disabilities (Lee et al., 2017; Azenkot et al., 2016;
Axelsson et al., 2019; Arevalo Arboleda et al., 2021). Participatory design
has also been successfully used to develop autonomous social robots (Winkle et
al., 2021; Tian et al., 2021; Axelsson et al., 2021). Participatory design,
however, has not been used in the development of trust measures. As described
in Section 2, prior approaches were created based initially on an author’s
expertise, from SMEs input, or a literature review of past trust scales.
Subsequently, some of those were evaluated for their validity with
undergraduate students or online MTurk workers. A participatory approach would
instead engage individuals in the community for whom a robot system is
intended to benefit to help co-design the trust measure.

First, the community members would identify the relevant attributes that could
affect their trust in a robot, which would be used to produce an initial set
of questions. Next, the community members would use the trust measure to
evaluate their trust after a physical interaction task with a robot. A
questionnaire would then ask them to evaluate the appropriateness of each
question (e.g., on a 3-point Likert rating scale “Not necessary”, “Useful, but
not essential”, and “Essential” (Lawshe et al., 1975)). A semi-structured
interview would also be used to analyze the trust measure’s validity – do the
questions allow the participants to express the level of trust they felt
during the interaction? Together, the quantitative and qualitative results
would be used to revise the initial measure to reflect the participants’
feedback. This process would then be iteratively repeated in a cycle with the
participants using the revised measure to evaluate their trust, followed by a
quantitative and qualitative examination of their experience used to further
revise the measure. The participatory design process would stop when the
measure converges to a consistent set of questions that accurately represent
the attributes that affect trust in that community.

Only one work used a similar participatory process but to develop an initial
understanding of trust in the context of robot assistants for elderly care
(Schwaninger et al., 2021). In this work, a card game was designed and used
during interviews to elicit trust-related factors from older adults
(Schwaninger et al., 2021). The card game allowed researchers to ask about
specific trust characteristics but also facilitated open conversation about
trust (Schwaninger et al., 2021). The results showed that this methodology
allowed them to identify the specific dimensions of trust that were relevant
to the community they were investigating, such as privacy, control,
companionship, and skepticism (Schwaninger et al., 2021). As the authors of
that work state, this research represents the initial ideation phase of
participatory design, which corresponds to the initial step of the
participatory process proposed here. They did not, however, use the findings
to produce a trust measure, as proposed here

Although there are potential risks in the use of participatory design, such as
possible physical or mental harm, risk of exploitation, and removal of agency
(Zytko and Louie, 2022), there are also potential benefits, such as bringing
visibility to marginalized people in HRI, addressing social justice issues,
and revealing HRI researchers internal biases (Lee et al., 2022). For example,
a participatory design process that involves queer people could result in a
culturally-competent tool (Fine and Torre, 2019), more inclusive language
(Meyer and Elias, 2022), center their experiences (Korpan, 2023; Stolp-Smith
and Williams, 2024), and ultimately produce a technology that they trust more
(Haimson et al., 2020, 2023).

The use of participatory design would also support a recent recommendation to
center the human experience of trust in the development of a framework for
human-robot trust (Lange et al., 2023). It could allow for the development of
ad hoc trust measures that address other circumstances that impact trust:
repeated interactions, imperfect interactions, subconscious influences on
perception, conformity to social norms, and environmental factors (Holthaus
and Rossi, 2023). It could also produce context-based questions that address
different individual identity factors that affect trust (Korpan, 2023) and how
those relate to aspects of the robot, task, environment, and other agents
(Holthaus and Rossi, 2023). It could also potentially weigh the importance of
trust items differently based on community differences (Korpan, 2023).

###  3.2. Social Justice Framework

Robotics researchers’ choices reflect the values and beliefs of society, both
the good and bad (Michalec et al., 2021). For example, the deployment of
language-capable robots poses several potential risks, such as overtrust being
manipulated to influence human morals, reinforcement of gendered and
racialized biases, and perpetuation of harmful assumptions about human
identity (Williams et al., 2023). HRI studies also often fail to broadly
address diversity and inclusion in both the human subjects used and the
systems created (Seaborn, 2023; Winkle et al., 2023a). A systematic review of
HRI research from 2006 to 2022 found that over 90% of papers used “WEIRD”
(Western, Educated, Industrial, Rich, and Democratic) participants (Seaborn et
al., 2023). Furthermore, a robot’s stereotypical (gendered) appearance have
been shown to activate human stereotypes, which subsequently affects people’s
behavior with those robots and their trust in them (Perugia et al., 2023;
Perugia and Lisy, 2023). As a result of these pervasive challenges in how HRI
research is conducted, robots are designed and deployed, and who they are
evaluated with, a social justice-oriented approach could result in a more
equitable HRI (Zhu et al., 2024).

The Robots for Social Justice framework recommends five considerations for HRI
researchers: what the community the research is intended to benefit, what
human capabilities would be enhanced, how those capabilities are valued and
prioritized, what structural conditions are present in that community, and
what are the power structures that surround and influence the community (Zhu
et al., 2024). A social justice-oriented process for the development of a
human-robot trust measure should then be grounded in the context in which it
will be used. It should begin with a specification of the community for which
trust will be measured, consider what human capabilities are being enhanced by
the robot, and prioritize the needs of that community as defined by them. As
the community is engaged in this development process, the structural and
systematic limitations and the power structures that exist in the community
should be critically examined. This would ensure that the developed trust
measure considers the potential risks and harms that could result if it fails
to capture important trust attributes for that community, given its unique
circumstances. A participatory design process could easily integrate with this
social justice framework to produce a measure of trust that addresses the
social and ethical challenges of HRI research.

##  4\. Conclusion

Human trust in a robot is a difficult construct to measure because of its
complexity. Many different measurement tools have been created and validated
in a variety of ways. None of the past approaches, however, addressed the
power structures or systems of inequity that HRI research can reinforce. This
position paper proposes the use of a participatory design process in the
context of a social justice framework would result in a validated trust
measure that captures the relevant factors that affect trust for a community.

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