# User Characteristics in Explainable AI: The Rabbit Hole of Personalization?

Robert Nimmo  0009-0006-5857-6469 University of GlasgowGlasgowUnited Kingdom
<EMAIL_ADDRESS>,  Marios Constantinides  0000-0003-1454-0641 Nokia
Bell LabsCambridgeUnited Kingdom<EMAIL_ADDRESS>,
Ke Zhou  0000-0001-7177-9152 Nokia Bell LabsCambridgeUnited Kingdom
<EMAIL_ADDRESS>,  Daniele Quercia  0000-0001-9461-5804 Nokia Bell
LabsCambridgeUnited Kingdom<EMAIL_ADDRESS>and  Simone
Stumpf  0000-0001-6482-1973 University of GlasgowGlasgowUnited Kingdom
<EMAIL_ADDRESS>
###### Abstract.

As Artificial Intelligence (AI) becomes ubiquitous, the need for Explainable
AI (XAI) has become critical for transparency and trust among users. A
significant challenge in XAI is catering to diverse users, such as data
scientists, domain experts, and end-users. Recent research has started to
investigate how users’ characteristics impact interactions with and user
experience of explanations, with a view to personalizing XAI. However, are we
heading down a rabbit hole by focusing on unimportant details? Our research
aimed to investigate how user characteristics are related to using,
understanding, and trusting an AI system that provides explanations. Our
empirical study with 149 participants who interacted with an XAI system that
flagged inappropriate comments showed that very few user characteristics
mattered; only age and the personality trait openness influenced actual
understanding. Our work provides evidence to reorient user-focused XAI
research and question the pursuit of personalized XAI based on fine-grained
user characteristics.

Disclaimer: This paper contains examples of language that some people may find
offensive.

explainable AI, trust, personalization, human-centered artificial
intelligence, user characteristics

††ccs: Human-centered computing Empirical studies in HCI††ccs: Human-centered
computing Interactive systems and tools††ccs: Computing methodologies Machine
learning

##  1\. Introduction

Artificial Intelligence (AI) applications are increasingly becoming ubiquitous
(Abdul et al., 2018), from identifying spam emails to determining eligibility
for loans to generating language. With the rapid advancements of AI and the
seamless integration into people’s lives, the need for Explainable AI (XAI)
has become critical to foster transparency and trust (Gunning et al., 2019).
This need for transparency and trust is more than evident not least because of
the potential repercussions of automated decisions on people’s lives and the
increasing regulatory oversight of AI systems (Kissinger et al., 2021; Barredo
Arrieta et al., 2020; Tahaei et al., 2023).

To respond to this need, the field of XAI has made substantial progress in
developing methods for providing explanations, such as global and local
explanations for AI models (Ribeiro et al., 2016; Lundberg and Lee, 2017).
Global explanations offer an overview of the model’s functioning, whereas
local explanations delve into how specific predictions were generated for
individual instances. Explanations in AI systems can assist end users in
constructing accurate mental models of AI systems, thereby enhancing both
their understanding and the degree of trust attributed to these systems (Lim
et al., 2009; Kulesza et al., 2013). These methods have enhanced users’
comprehension of AI systems by ‘opening up’ their black-box inner workings
(Gunning et al., 2019).

At the same time, there is growing evidence suggesting that a user’s
characteristics may have an impact on how explanations are received (Ehsan et
al., 2021a; Jiang et al., 2022; Stumpf et al., 2008). A user’s characteristics
typically consist of a broad range of attributes, including but not limited to
age, gender, personality, and experience. The influence of user
characteristics has been studied in other fields, for example, personality has
been extensively studied in the context of recommender systems (Tintarev and
Masthoff, 2007; Tkalcic and Chen, 2015; Dhelim et al., 2022) and Human-Robot
Interaction (Craenen et al., 2018b, a). Information retrieval research has
revealed that previous experience impacts search behavior (Yan et al., 2006;
Ranganathan et al., 2009; White et al., 2009; Soulier et al., 2014; Mao et
al., 2018). Specific to XAI, gender and educational background have been found
to affect the types of explanations that participants preferred as well as
their trust (Reeder et al., 2023). Personality traits have been found to
impact the interaction with explanations (Millecamp et al., 2019, 2020). This
has provided some impetus to work that aims to employ users’ characteristics
to personalize XAI and tailor the design and presentation of explanations to
users (Conati et al., 2021).

While these works provide some evidence about a potential link between user
characteristics and AI explanations, our understanding remains limited.
Therefore, we set out to test whether user characteristics are associated with
user engagement with explanations, the understanding of these explanations,
and the degree of trust users attribute to AI systems. In so doing, we
developed an interactive AI system that provides local explanations for
flagging inappropriate comments, and formulated four Research Questions (RQs):

  1. RQ1:

How do user characteristics relate to user engagement in finding
misclassifications?

  2. RQ2:

How do user characteristics relate to the perceived understanding of
explanations?

  3. RQ3:

How do user characteristics relate to the actual understanding of
explanations?

  4. RQ4:

How do user characteristics relate to the degree of trust users attribute to
an AI system?

In addressing these questions, we make three main contributions:

  * •

We provide a prototype that provides local explanations in the task of
flagging inappropriate comments (section 3.1), which we release open-source.
The prototype guides participants through the user study and logs users’
interactions. We utilized a pre-existing multi-class classifier named Detoxify
(Hanu and Unitary team, 2020) with our prototype, where we generated
explanations for each comment. Participants interacted and provided feedback
on the explanations provided using the prototype.

  * •

We advance the methodology of investigating user characteristics in XAI. We
conducted a large-scale study by engaging 149 participants recruited from
Prolific (section 3.3). We utilized our prototype to investigate the effect
users’ characteristics - specifically age, gender, previous experience, and
the Big 5 personality traits - have on outcome measures, such as participants’
engagement, trust, and perceived and actual understanding of the XAI system.

  * •

We show empirical evidence that very few user characteristics are related to
our outcome measures of engagement, trust, and understanding. Only age and the
personality trait openness were associated with actual understanding (section
4).

In light of these results, we argue to reorient user-focused XAI research in
pursuit of personalized XAI based on fine-grained user characteristics
(section 5).

##  2\. Related Work

###  2.1. Transparency, Understanding, and Explanations

Many AI systems are difficult to understand as they typically are ‘black-box
models’ (Adadi and Berrada, 2018). The need for transparent AI systems has
become increasingly recognized (Barredo Arrieta et al., 2020) in the context
of responsible AI systems where explanations are commonly employed to increase
the transparency of AI systems. It has been suggested that explanations help
to evaluate if an AI system meets fairness, privacy, reliability, causality,
and trust which can help to expose potential issues in an AI system during
development before being deployed to the end-user (Doshi-Velez and Kim, 2017).
Explanations can also help end-users understand how the underlying AI model
works (Lim et al., 2009; Kulesza et al., 2013), leading to higher user
satisfaction and better-calibrated reliance on AI systems (Dzindolet et al.,
2003; Cramer et al., 2008), reducing the potential of algorithm aversion (De-
Arteaga et al., 2020), obtaining better feedback and input to an AI system
(Kulesza et al., 2015) and increasing trust (El Bekri et al., 2020; Barredo
Arrieta et al., 2020; Petch et al., 2022).

Explanations are designed to help the user understand how the AI model
behaves, by providing context on how and why a specific decision was made
(local explanation) or by explaining how the model works (global explanation)
(Gunning et al., 2019; Liao et al., 2021; Dodge et al., 2019; Ehsan et al.,
2021b). Explanations frequently target users’ _mental models_ (Norman, 1987;
Kulesza et al., 2013) which are an individual’s internal representation or
model of how a system works, which gets updated every time the person
interacts with an AI system. Mental models assist individuals in
understanding, explaining, and predicting events and determining appropriate
actions. Lack of transparency does not prevent users from building a mental
model, although it can lead to an inaccurate mental model being built. When
providing explanations, it has been suggested that they need to cover a set of
‘intelligibility types’ (Lim and Dey, 2009; Kulesza et al., 2011) which flesh
out the user’s mental model, addressing _Why_ (why did the AI system do X?),
_Why not_ (why did the AI system not do Y?), _What if_ (what would the AI
system do if Z happens?), _How_ (how does the AI system do Y?), and _What_
(what did the AI system do?).

Two of the most popular explanation approaches, addressing the Why aspect of
explanations, include SHapley Additive exPlanations (SHAP) (Lundberg and Lee,
2017) and Local Interpretable Model-Agnostic Explanations (LIME) (Ribeiro et
al., 2016). Both approaches are model-agnostic approaches, allowing them to
work for any model. SHAP explanations provide a local and global explanation
of the given model. SHAP is an extension of Shapley Values (Shapley, 1951), a
solution from cooperative game theory. SHAP explanations are more complex and
thus more difficult to understand than LIME explanations; many applications
using SHAP are targeted at AI experts. Instead, LIME provides a local
explanation of the given model by showing the most important features used in
making the decision; they have shown good usability and interpretability for a
range of users.

When developing explanations, there is no “one-fits-all approach” (Liao and
Varshney, 2022; Gunning et al., 2019). Previous research has indicated that
current XAI systems are often designed based on the researcher’s or designer’s
subjective perception of what constitutes a ‘good’ explanation rather than
being tailored to the needs of the end-user (Miller et al., 2017; Miller,
2019). Instead, explanations should be developed based on the needs of the
users and the purpose of the explanation through a human-centered approach,
being specific to ‘who’ the explanation will be seen by and ‘why’ they require
the explanation (Ehsan and Riedl, 2020). Consequently, a number of research
efforts have tried to uncover user characteristics that matter in interacting
with explanations, with a view to personalizing explanations to a specific
user.

###  2.2. User Characteristics and Personalization

User characteristics have frequently been used to personalize experiences,
interactions, or recommendations. A new direction is to use the same principle
of personalization for XAI, in which the design and presentation of
explanations are tailored to users (Conati et al., 2021).

A user can have many different user characteristics, such as age, gender,
previous experience, and personality. We present an overview of how these user
characteristics have been employed in research towards personalization. It has
been found that _gender_ can influence the perception and experience of using
recommender systems (Knijnenburg et al., 2012). Gender and educational
background have also been found to affect the types of explanations that
participants preferred as well as their trust (Reeder et al., 2023). However,
it has also been cautioned that gender might not be as important as previously
thought and requires more research to confirm any effect (Tran et al., 2023).
A common user characteristic that is captured during research studies is
_age_. Although not proven to be significant, age has been considered in the
willingness to accept advice (Jiang et al., 2022). _Previous experience_ has
been found to influence search behavior for information (Yan et al., 2006;
Ranganathan et al., 2009; White et al., 2009; Soulier et al., 2014; Mao et
al., 2018), how interactions occur with recommendations in a recommender
system (Cai et al., 2022) and also preferences for the presentation format of
explanations and understanding of explanations (Szymanski et al., 2021).
However, other research has revealed no bearing of previous experience with AI
and with interaction with an XAI system (Leichtmann et al., 2023).

_Personality_ has recently emerged as an area of focus for research in
personalizing AI or XAI. In recommender systems, personality has been used to
suggest appropriate items where there is little information about a user or an
item to give meaningful recommendations (Tintarev et al., 2013; Tkalcic and
Chen, 2015; Liao et al., 2022). In addition, personality has also been used in
Human-Robot Interaction (HRI) research to improve users’ perception and
interactions with robots (Craenen et al., 2018a, b). This includes looking at
the role personality plays in AI anxiety (Kaya et al., 2022) and how
personality can affect perception and trust in recommender systems
recommendations (Cai et al., 2022). Little research has been conducted to
investigate how personality affects XAI but personality traits have been found
to impact the interaction with explanations (Millecamp et al., 2019, 2020;
Conati et al., 2021).

###  2.3. Research Gap

Previous research has indicated that user characteristics might impact various
aspects of system use. Considering the growing importance of AI systems and
the imperative to provide explanations, there have been attempts to
investigate how user characteristics influence users’ perception and
understanding of such explanations. However, our understanding of whether and
how different user characteristics are associated with the effectiveness of AI
explanations remains limited. Therefore, investigating this can potentially
improve explanation design and lead to the creation of personalized
explanations.

##  3\. Methods

To investigate our research questions, we ran an empirical, online study in
which 149 participants interacted with an AI prototype that classified
Internet comments as ‘toxic’ or ‘non-toxic’ for flagging inappropriate
comments for humans to check. This is a commonplace task, similar to other
binary classification systems such as spam filters, where human-in-the-loop
input is beneficial. The interface offered explanations and the ability to
make suggestions as to how to improve the system, inspired by the explanatory
debugging approach (Kulesza et al., 2015). We collected the participants’
characteristics as well as prototype usage information, understanding and
trust for statistical analyses.

###  3.1. Prototype

####  3.1.1. The Dataset

We decided to use a hate speech dataset, Jigsaw (cjadams et al., 2017), for
our study. This dataset is freely available and several open-source pre-
trained models have been developed on this dataset. In addition, the dataset
does not require prior knowledge or domain specialization in a particular
field so is ideally suited for user studies with lay people.

It contains 220,000 Wikipedia talk page edit comments over one year, which
have been labeled by human raters for toxic behavior in six categories. These
categories included _toxic_ , _severe toxic_ , _obscene_ , _threat_ , _insult_
, and _identity hate_. For our study, we filtered out comments with high
(¿0.75) obscene or identity hate scores in order to mitigate harm to our
participants. With the filtered toxic/non-toxic Jigsaw test dataset, we
randomly sampled 200 comments while keeping an equal class distribution
between toxic and non-toxic comments, using the Python library Pandas built-in
function. Further, we removed any comments that were too short (if the comment
was less than three words), not understandable (if the comment is written in a
language other than English or containing numerous misspellings, rendering the
comment illegible), or inappropriate (if the comment fell into removed
categories through a manual review to satisfy ethics requirements). This
resulted in 100 comments which were shown to the study participants.

####  3.1.2. The AI model

We employed a pre-existing AI classifier named Detoxify (Hanu and Unitary
team, 2020). This model is a multi-class classifier and was trained on the
Jigsaw training dataset using a BERT base model (uncased) transformer from
Hugging Face (Devlin et al., 2019). During each training step, a batch size of
10 was utilized. For each input, the data was tokenized using the Bert
tokenizer. A binary cross-entropy loss function was used to optimize the model
between each step. We selected this model as it was open-source and pre-
trained, allowing us to quickly implement the model in our prototype and
develop explanations. The model achieved an AUC score of 98 (Hanu and Unitary
team, 2020); thus, it still produces misclassification to engage our
participants. Our research focus concentrated on reactions to explanations
instead of model accuracy; this also influenced our choice of using a state-
of-the-art classifier to minimize the impact of lower model accuracy on
measures under investigation, such as trust.

####  3.1.3. Explanations

We decided to use Local Interpretable Model-Agnostic Explanations (LIME)
(Ribeiro et al., 2016) to explain our model. This is an approach that has been
frequently employed to explain deep learning models such as the one we
employed in our study.

To generate our explanations, we used the Python implementation of LIME
(Ribeiro, 2023). The LimeTextExplainer was used to explain our predictions for
each comment in our testing dataset. The LIME explanation generated a list of
the top 10 most important words and their weights for each comment, which were
stored in the prototype. Table 1 provides an example of the LIME output for
the top 10 most significant words generated by Lime. The higher the word
weight, the more significant the word was to the prediction. These
explanations were saved in a JSON file to be used in our prototype.

Table 1. Top 10 most important words for the comment “Ah, the wonders of political correctness. Bastard, a perfectly good word for centuries, is no longer to be used. Where do you suppose it will end?” Word |  Weight |  Label
---|---|---
Bastard |  .829 |  Toxic
perfectly |  .030 |  Toxic
the |  .021 |  Non-toxic
Where |  .020 |  Toxic
good |  .016 |  Non-toxic
Ah |  .013 |  Non-toxic
political |  .012 |  Non-toxic
is |  .011 |  Non-toxic
centuries |  .008 |  Non-toxic
used |  .002 |  Toxic

####  3.1.4. Interface

Figure 1. The prototype interface. (A) A list of all the comments to be
assessed. (B) The selected comment and a bar chart representing the top 10
most important words in the selected comment are sorted in descending order.
(C) Ability to add new important words that are not already highlighted. (D) A
summary of the AI system’s prediction for the selected comment and the ability
to change the predicted label. (E) A list of the top 10 most important words
in the selected comment. For each important word, the user can change the
label (which label, Toxic/Non-toxic, is most associated with the word) and the
word importance (how important the word is in the prediction).

The prototype interface: The interface includes on the far left a list of all
the comments to be assessed. On clicking a comment in this list, the selected
comment text is shown to the right of the list, with words having a higher
correlation with the non-toxic label highlighted in blue, or in red for the
toxic label. A bar chart representing the top 10 most important words in the
selected comment are shown below the comment sorted in descending order, again
color-coded in blue or red to show the relevance to the label. Underneath the
bar chart, the user has the ability to add new important words that are not
already highlighted by selecting them from a drop-down list, adding the label,
and using a slider to give the importance of this word. To the far right of
the interface is a summary of the AI system’s prediction for the selected
comment and the ability to change the predicted label by selecting it through
a drop-down. Underneath this is a list of the top 10 most important words in
the selected comment (similar to the bar chart) but for each important word,
the user can change the label through a drop-down and the word importance
through a slider.

Inspired by work on explanatory debugging (Kulesza et al., 2015; Stumpf et
al., 2009; Kulesza et al., 2009, 2010), we decided to develop an interface
that might be used in a task to review automatically flagged comments (Figure
1).

The interface is comprised of a list of comments to check (Component A), and
its predicted class. Based on the user’s selection, the comment text is
displayed next to it (Component B), with the top 10 most important words
highlighted in the comment, color-coded according to class. Underneath the
comment text, it is displayed a bar chart to visualize the weight each
important word had on the prediction. To the right of the comment text, we
showed the predicted class and the Detoxify model prediction confidence. The
user is able to agree with this class prediction or assign a different label
(Component D). To provide additional feedback to the system, the user is able
to add new words not highlighted in the comment (Component C), or adjust the
weight and class of words in the comment (Component E). The user can reset any
changes made, or mark the comment as checked. Comments were presented in the
same order to all participants. Given the purpose of the study was to
investigate whether any user characteristics are associated with users’
perception and understanding of explanations, this choice allowed us to
eliminate any confounding factors that might have been introduced by varying
the order of the comments.

####  3.1.5. Prototype Implementation

We developed the prototype as a web app, accessible through a URL. The
prototype is modular and reusable in the future for conducting similar user
studies. It utilizes serverless functions, specifically Cloudflare Worker
Functions, a MongoDB database as the backend and database, and a React web app
frontend. Using serverless functions allows future studies to decide the
specific backend architecture language they would like to use, as all requests
to the backend are completed through API requests. In addition, serverless
functions are quicker, easier, and cheaper to deploy and require less setup
during the deployment configuration. Lastly, they are scalable, being able to
cope with a large number of participants in a short time span. MongoDB was
selected due to its flexibility, scalability, and ease of use, being more
suitable for storing long user logs with its document-oriented approach that
closely matched the collected user log data structure. The React web app
frontend was chosen for its flexibility and ease of development. React’s
component-based architecture allowed us to easily modularise the web apps,
allowing the prototype to be customized easily for future user studies. It
also allowed us to build a responsive and interactive user interface, which is
crucial for a good user experience.

###  3.2. Participants

To determine the required sample size for our analyses, we used
G*Power.111https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-
psychologie-und-arbeitspsychologie/gpower We found that at least 117
participants were required to analyze our independent variables (i.e., age,
gender, previous experience with AI, and the five personality traits) using
two-tailed regressions at a minimum of 0.05 level of significance, 95% level
of power, and an effect size of 0.2. To account for any data loss due to
quality checks or missing data, we further increased our sample size. By
conducting a post-hoc sensitivity analysis, we found that our analyses had
even more strict effect size of 0.15.

We thus recruited 150 participants for our user study through Prolific
(https://www.prolific.co/), an online platform to recruit participants for
research studies. One participant had incomplete demographic information and
was therefore excluded from the analysis, thus resulting in 149 participants
in our data set.

Our participants’ ages ranged from 18 to 67, with a median age of 36
($\sigma=11.5$). There were 75 male participants and 74 female participants in
total. Participants needed to be fluent in English in order to judge the
toxicity of language in a comment and we enforced this through a screener
question and by restricting the residence requirement to majority-English
speaking countries. In keeping with our use case of moderating comments, we
did not restrict participants to have AI knowledge.

Participants were compensated £4.50, consistent with Prolific’s payment
principles of ‘ethical rewards’ and in line with the United Kingdom national
minimum wage at the time the study was conducted. We obtained informed
consent; our user study was considered and approved by the University of
Glasgow School of Computing Science Research Ethics Committee.

###  3.3. Study Task

Figure 2.  Our user study procedure consists of six steps. Step 1 \- Study
introduction: The participants were provided with an overview of the study’s
objectives, the potential outcomes of their involvement, the utilization of
their data, as well as the advantages and disadvantages of participation. Step
2 \- Pre-task questionnaire: The participants completed a pre-task
questionnaire consisting of two sections to measure their personality and
previous experience with AI systems. Step 3 \- Task introduction: Participants
were presented with a scenario and an introduction to the task that they were
required to complete for the prototype. Step 4 \- Practice task: Participants
were instructed to complete a practice task using the explanatory debugging
interface. Step 5 \- Main task: The primary task involved the utilization of
the explanatory debugging interface to complete the study task. Step 6 \-
Post-task questionnaire: Participants were requested to fill out a post-task
questionnaire to measure the participant’s trust, perceived understanding, and
actual understanding.

Our user study procedure consists of six consecutive steps: Step 1. Study
introduction: The participants were provided with an overview of the study’s
objectives, the potential outcomes of their involvement, the utilization of
their data, as well as the advantages and disadvantages of participation. Step
2. Pre-task questionnaire: The participants completed a pre-task questionnaire
consisting of two sections to measure their personality and previous
experience with AI systems. Step 3. Task introduction: Participants were
presented with a scenario and an introduction to the task that they were
required to complete for the prototype. Step 4. Practice task: Participants
were instructed to complete a practice task using the explanatory debugging
interface. Step 5. Main task: The primary task involved the utilization of the
explanatory debugging interface to complete the study task. Step 6. Post-task
questionnaire: Participants were requested to fill out a post-task
questionnaire to measure the participant’s trust, perceived understanding, and
actual understanding.

After participants agreed to the HIT on Prolific, they were directed to our
study site. The user study consisted of six steps summarized by Figure 2,
which took about 30 minutes to complete.

We first familiarized participants with the study and obtained informed
consent. We then presented them with a pre-task questionnaire consisting of
two sections. In the first section, we captured a participant’s personality
traits using TIPI (Gosling et al., 2003). The second section employed the
level of expertise scale (Mcbeath, 2011) for, respectively, AI systems,
machine learning, and explanations. After this, participants were introduced
to their task: _“You are currently working for a social media platform like
Reddit. The company has just deployed a new AI system that automatically flags
a comment as Toxic or Non-toxic.”_ , and given instructions on how to use the
prototype interface. In order to facilitate familiarity and proficiency in
using the interface, participants were instructed to complete a practice task.
The interface for the practice task mirrored that of the main task, but it
only featured one comment to check and incorporated two explicit attention
checks, which each participant was asked to complete. Participants were then
directed to the main task interface (Figure 1). The prototype presented
participants with 100 comments to check, with 61 toxic comments and 39 non-
toxic comments for our study’s final class distribution. Each participant was
allotted 20 minutes to complete the main study task, which entailed evaluating
as many of the 100 comments supplied as possible. Participants were not
expected to examine every comment but rather to spend time to thoroughly think
through each comment. Once the time elapsed they were directed to the post-
task questionnaire. There, participants were asked about their trust in the
system using the trust scale recommended for XAI (Hoffman et al., 2019) and
rated their perceived level of understanding regarding the AI system. They
also completed a series of 12 questions to ascertain their actual level of
understanding, similar to previous work by Kulesza et al. (Kulesza et al.,
2012) to measure the mental model score. These questions covered nine key
criteria regarding the AI system. These criteria included understanding of the
user interface, feature weights, feature labels, adjusting feature weights,
classes, adding new words, confidence, how predictions are made, and what
class might be predicted based on features. Nine of the questions were
multiple-choice, while three questions allowed for open-ended answers.

###  3.4. Data Collection and Analysis

One of the most common ways to characterize personality is the five-factor
model of personality (Big 5) (McCrae and Costa, 1987), where personality is
measured on five dimensions:

  * •

Openness: measures how curious, original, and open to new ideas an individual
is.

  * •

Conscientiousness: measures how self-disciplined and hardworking an individual
is.

  * •

Extraversion: measures how sociable, outgoing, and assertive an individual is.

  * •

Agreeableness: measures how cooperative and good-natured an individual is.

  * •

Neuroticism: measures an individual tendency towards unstable emotions.

Many questionnaires have been developed of different lengths and accuracy to
measure a participant’s personality against the Big 5. One of the most well-
established and used methods is the Big Five Inventory (BFI) (John et al.,
1991; Benet-Martínez and John, 1998; John et al., 2008). The BFI is a 44-item
questionnaire which consists of participants answering each question with a
5-point Likert scale. However, in studies where time is limited, the Ten-Item
Personality Inventory (TIPI) of measuring a participant’s personality against
the Big 5 has been used (Gosling et al., 2003). Although similar to BFI, it
only includes ten items and utilizes a 7-point Likert scale. It has been shown
that TIPI gathers similar results compared to BFI but can be less robust for
the openness dimension (Gosling et al., 2003). In our study, we chose to
employ the TIPI questionnaire.

The responses from the pre-task questionnaire were processed as follows: we
calculated participants’ personality scores on the 5 dimensions using the
responses from the TIPI questionnaire and the method to calculate each
personality dimension set out by Gosling et al. (Gosling et al., 2003). The
participants self-rated their level of experience with AI systems, ranging
from 0 to 10. In our sample, participants previous experience ranged from 1 to
6 with a $\mu=3.15$ and $\sigma=.964$ (95% of the values were in the range 1
to 4). While our sample skews towards lower previous experience, this reflects
use cases where AI explanations are targeted at non-expert users.

From the post-task questionnaire, we obtained their system trust score, their
perceived understanding score and their actual understanding score. The trust
survey consisted of eight questions that assessed the participants’ trust in
the XAI system, utilizing a 5-point Likert scale ranging from 1 (strongly
disagree) to 5 (strongly agree). To obtain the participants’ overall trust
score, the sum of the eight questions was calculated, with the scale reversed
for question six. A participant could score a maximum of 40 (reflecting high
trust in the AI system) and a minimum of 8 (reflecting low trust in the AI
system). For the perceived understanding, we recorded an integer using a
5-point Likert scale that ranged from 1 (did not understand at all) to 5
(fully understood). All questionnaires used in the study were made available
as supplementary material.

An automated script was used to grade the multiple-choice answers for actual
understanding, awarding one mark for a correct response and zero for an
incorrect answer. A researcher reviewed and graded the open-ended
questionnaire responses using a predetermined marking scheme, allowing full
marks for correct reasons and for half marks for partially correct reasons.
The actual understanding score for each participant was calculated by summing
their points, with a maximum possible score of 15.

During the main study task, we logged every user interaction with the
prototype to keep track of all suggestions and changes the user made to the AI
system. We ensured continued engagement and high data quality through explicit
attention checks and checking the user logs for suspicious behavior, such as
prolonged intervals between reviewing each comment (5+ minutes) or rapidly
reviewing comments (within 1-3 seconds).

To answer our research questions, we conducted multiple regression analyses,
using user characteristics as independent variables and trust, perceived, and
actual understanding as dependent variables (or outcome measures). We assessed
the linearity by partial regression plots and a plot of studentized residuals
against the predicted values. The independence of residuals was assessed with
the Durbin-Watson statistic. We visually inspected a plot of studentized
residuals against unstandardized predicted values to assess homoscedasticity
in each multiple regression test. Multicollinearity was assessed for each
multiple regression test by checking tolerance values were greater than 0.1.
In addition, we examined studentized deleted residuals to ensure that no
values deviated by more than ±3 standard deviations, checked that leverage
values were below 0.2, and verified that values for Cook’s distance exceeded
1. Lastly, we checked the assumption of normality for the residual was met for
each of the multiple regression tests by Q-Q plots.

##  4\. Results

We first present the statistics of our participants, and then present a
correlation analysis between variables. We then show the results of our
regression analysis, aiming to scrutinize the impact of user
characteristics—age, gender, previous experience, and personality traits—on
the four dimensions of interest. These dimensions pertain to user engagement
with explanations, the perceived and actual understanding of these
explanations, and the degree of trust attributed to them.

###  4.1. Participants Statistics

The age distribution of our participants spanned from 18 to 67 years, with a
median age of 36 ($\sigma=11.5$), and the gender distribution was equally
divided (74 females).

On average, participants’ personality traits, on a scale from 1 to 7, were low
in extraversion ($\mu=3.31$ and $\sigma=1.53$), high in agreeableness
($\mu=5.03$ and $\sigma=1.16$), high in conscientiousness ($\mu=5.07$ and
$\sigma=1.35$), average in neuroticism ($\mu=4.27$ and $\sigma=1.49$) and high
in openness ($\mu=4.97$ and $\sigma=1.10$); these distributions conformed to
the standard personality values derived from a representative U.S. population
sample (Soto et al., 2011).

###  4.2. Correlations Between Variables

We conducted a correlation analysis to investigate any relationships within
our collected data. Table 2 shows the correlation coefficient _r_ in a
variable matrix. We found that there were some statistically significant
correlations within personality traits: extraversion was weakly positively
correlated with openness (_r_ =0.397), conscientiousness (_r_ =0.318),
neuroticism (_r_ =0.263) and agreeableness (_r_ =0.173), and neuroticism was
weakly positively correlated with conscientiousness (_r_ =0.394) and
agreeableness (_r_ =0.180). We also found a statistically significant weak
positive correlation between age and conscientiousness (_r_ =0.276), and a
weak negative correlation between gender and agreeableness (_r_ =-0.264). In
our regression models, we set female participants to 0 values and male
participants to 1 values. Looking at relationships within other user
characteristics, previous experience and gender were weakly positively
correlated (_r_ =0.215).

In terms of user characteristics and outcome measures, we found that actual
understanding was weakly negatively correlated with openness (_r_ =-0.191),
conscientiousness (_r_ =-0.198) and agreeableness (_r_ =-0.173), while
perceived understanding was weakly positively related to extraversion _r_
=0.169) and trust (_r_ =0.204). We therefore explored the relationships
between user characteristics and outcome measures more thoroughly through
regression models.

Table 2. Pairwise Spearman’s correlations among the independent variables Big-
Five personality traits, age, gender, and previous experience, and the
dependent variables trust, engagement, perceived understanding, and actual
understanding. The correlations that are statistically significant are in bold
and are marked with a number of *s based on their significance levels (i.e.,
*$p<.05$, **$p<.01$, ***$p<.001$). Significant results are discussed in the
text.

Variable |  | 1.
---
Openness
| 2.
---
Conscientiousness
| 3.
---
Extraversion
| 4.
---
Agreeableness
| 5.
---
Neuroticism
| 6.
---
Age
| 7.
---
Gender
| 8.
---
Prev Exp
| 9.
---
Trust
| 10.
---
Engagement
| 11.
---
Perc Und
| 12.
---
Act Und
1\. Openness | - |  |  |  |  |  |  |  |  |  |  |
2\. Conscientiousness | 0.105 | - |  |  |  |  |  |  |  |  |  |
3\. Extraversion | 0.397** | 0.318** | - |  |  |  |  |  |  |  |  |
4\. Agreeableness | 0.154 | 0.114 | 0.173* | - |  |  |  |  |  |  |  |
5\. Neuroticism | 0.076 | 0.394** | 0.263** | 0.180* | - |  |  |  |  |  |  |
6\. Age | 0.021 | 0.276** | 0.064 | 0.032 | 0.092 | - |  |  |  |  |  |
7\. Gender | -0.089 | -0.065 | -0.074 | -0.264** | 0.153 | -0.023 | - |  |  |  |  |
8\. Prev Exp | 0.104 | -0.075 | -0.015 | -0.141 | 0.115 | -0.074 | 0.215** | - |  |  |  |
9\. Trust | -0.128 | 0.087 | 0.061 | 0.145 | -0.034 | -0.134 | 0.021 | -0.027 | - |  |  |
10\. Engagement | 0.054 | 0.107 | 0.061 | 0.070 | 0.018 | -0.072 | 0.019 | -0.035 | -0.088 | - |  |
11\. Perc Und | 0.131 | 0.085 | 0.169* | 0.049 | 0.144 | -0.163* | 0.113 | 0.087 | 0.204* | -0.100 | - |
12\. Act Und | -0.191* | -0.198* | -0.140 | -0.173* | -0.074 | -0.240** | -0.007 | 0.006 | 0.029 | 0.194* | 0.014 | -

###  4.3. Impact of User Characteristics on Outcome Measures

Table 3. Multiple regression results for the impact of age, gender, and previous experience with AI on engagement, perceived understanding, actual understanding, and trust. B = unstandardized regression coefficient; Std. error = standard error of the coefficient; $\beta$ = standardized coefficient; $adj.R^{2}$ = adjusted $R^{2}$. Statistically significant results are in bold and are marked with a number of *s based on their significance levels (i.e., *$p<.05$, **$p<.01$, ***$p<.001$). Significant results are described in the text. Impact of age, gender, and previous experience with AI on engagement | $adj.R^{2}=-.017$ | $p=.926$ |  |
---|---|---|---|---
Predictor | $B$ | Std. error | $\beta$ |  $p$-value
Constant | 5.509 | 1.142 |  |
Age | -.002 | .020 | -.009 | .912
Gender | .320 | .474 | .058 | .501
Previous Experience | -.027 | .248 | -.009 | .913
Impact of age, gender, and previous experience with AI on perceived understanding | $adj.R^{2}=.024$ | $p=.091$ |  |
Predictor | $B$ | Std. error | $\beta$ |  $p$-value
Constant | 3.283 | .326 |  |
Age | -.011 | .006 | -.152 | .065
Gender | .140 | .135 | .086 | .304
Previous Experience | .073 | .071 | .086 | .306
Impact of age, gender, and previous experience with AI on actual understanding | $adj.R^{2}=.056$ | $\boldsymbol{p=.010*}$ |  |
Predictor | $B$ | Std. error | $\beta$ |  $p$-value
Constant | 13.868 | 1.067 |  |
Age | -.065 | .019 | -.275 | ¡.001***
Gender | .115 | .443 | .021 | .795
Previous Experience | -.130 | .231 | -.046 | .572
Impact of age, gender, and previous experience with AI on trust | $adj.R^{2}=-.004$ | $p=.506$ |  |
Predictor | $B$ | Std. error | $\beta$ |  $p$-value
Constant | 24.597 | 2.065 |  |
Age | -.054 | .037 | -.122 | .141
Gender | -.037 | .857 | -.004 | .966
Previous Experience | -.219 | .448 | -.041 | .626

Table 3 shows the results of multiple regressions to test the impact of age,
gender, and previous experience with AI on engagement, perceived
understanding, actual understanding, and trust. When we tested the association
between age, gender, previous experience, and our participants’ engagement
with explanations, we found no statistically significant effect. For perceived
understanding, our model had no overall predictive power.

For actual understanding, however, our model did show statistical
significance. Age was found to be statistically significant and negatively
associated with actual understanding.

For trust, we found no statistically significant effect, with none of age,
gender, and previous experience being statistically significant.

Table 4. Multiple regression results for the impact of personality traits and previous significant results on engagement, perceived understanding, actual understanding, and trust. B = unstandardized regression coefficient; Std. error = standard error of the coefficient; $\beta$ = standardized coefficient; $adj.R^{2}$ = adjusted $R^{2}$. Statistically significant results are in bold and are marked with a number of *s based on their significance levels (i.e., *$p<.05$, **$p<.01$, ***$p<.001$). Significant results described in text. Impact of personality traits with AI on engagement | $adj.R^{2}=-.015$ | $p=.726$ |  |
---|---|---|---|---
Predictor | $B$ | Std. error | $\beta$ |  $p$-value
Constant | 4.850 | 1.573 |  |
Openness | -.014 | .231 | -.006 | .951
Conscientiousness | .145 | .195 | .070 | .456
Extraversion | .162 | .176 | .089 | .358
Agreeableness | -.150 | .205 | -.062 | .466
Neuroticism | .047 | .174 | .025 | .788
Impact of personality traits with AI on perceived understanding | $adj.R^{2}=.015$ | $p=.210$ |  |
Predictor | $B$ | Std. error | $\beta$ |  $p$-value
Constant | 2.289 | .452 |  |
Openness | .087 | .067 | .117 | .196
Conscientiousness | .020 | .056 | .033 | .724
Extraversion | .029 | .051 | .054 | .572
Agreeableness | -.003 | .059 | -.004 | .963
Neuroticism | .066 | .050 | .121 | .191
Impact of personality traits and age with AI on actual understanding | $adj.R^{2}=.111$ | $\boldsymbol{p=<.001***}$ |  |
Predictor | $B$ | Std. error | $\beta$ |  $p$-value
Constant | 17.755 | 1.512 |  |
Openness | -.472 | .210 | -.192 | .026*
Conscientiousness | -.268 | .181 | -.134 | .142
Extraversion | -.031 | .160 | -.018 | .846
Agreeableness | -.227 | .186 | -.097 | .225
Neuroticism | .076 | .158 | .042 | .630
Age | -.055 | .019 | -.231 | .005**
Impact of personality traits with AI on trust | $adj.R^{2}=.037$ | $p=.063$ |  |
Predictor | $B$ | Std. error | $\beta$ |  $p$-value
Constant | 20.836 | 2.789 |  |
Openness | -.849 | .410 | -.184 | .041*
Conscientiousness | .494 | .345 | .131 | .154
Extraversion | .283 | .312 | .085 | .366
Agreeableness | .681 | .363 | .156 | .063
Neuroticism | -.380 | .309 | -.112 | .220

Table 4 shows the results of multiple regressions to test the impact of
personality traits and age (previous significant result, Table 3) on
engagement, perceived understanding, actual understanding, and trust. When
tested the association between the five personality traits and our
participants’ engagement with explanations, we found no statistically
significant effect. Similarly, a model predicting perceived understanding had
no statistically significant effect.

However, the picture was slightly different for predicting actual
understanding of the explanations, with a statistically significant effect.
Given that age was found to be significant, we also included it in this model.
Out of the five personality traits, only openness had a significantly
statistical negative effect, while age was weakly and negatively associated
with actual understanding.

For trust, we again found no statistically significant effect. However, as in
the previous model, the trait of openness had a negative effect on the degree
of trust our participants attributed to the explanations.

In addition to the previous models, we tested a multiple regression model with
all variables combined (i.e., age, gender, previous experience, and the five
personality traits) to predict our participants’ engagement, perceived
understanding, actual understanding, and trust in explanations. To assess the
absence of multicollinearity among the independent variables, we conducted an
analysis using Variance Inflation Factors (VIF) (Miles, 2014). Typically, VIF
values exceeding 5 indicate potential multicollinearity concerns. However, in
our study, the highest VIF value calculated for any independent variable was
1.39, comfortably below this threshold, suggesting no significant
multicollinearity issues (Miles, 2014). Upon analyzing the full model for
engagement, we observed an inadequate fit ($F(8,140)=0.419$, $p=.908$,
$adj.R^{2}=-.032$), with none of the independent variables showing
significance – a result consistent with our findings from running the models
separately. For perceived understanding, the model marginally fit
($F(8,140)=2.027$, $p=.047$, $adj.R^{2}=-.053$), with age being the only
statistically significant predictor. For actual understanding, the model fit
(similar to running the models separately), with age and openness being the
only two statistically significant predictors ($F(8,140)=3.107$, $p=.003$,
$adj.R^{2}=-.102$). For trust, the model did not fit compared to running the
models separately ($F(8,140)=1.804$, $p=.081$, $adj.R^{2}=-.042$). Overall,
the model for predicting perceived understanding ($adj.R^{2}=0.053$) and trust
($adj.R^{2}=0.042$) had slight differences compared to running the models with
a subset of predictors. However, these differences can be considered
negligible as both models’ predictive power is relatively small. Additionally,
out of the four new models, the same set of variables was statistically
significant (i.e., age and openness), reinforcing our finding that no other
user characteristics had any predictive power.

##  5\. Discussion

The aim of our study was to investigate how user characteristics, such as
gender, age, expertise, and personality traits affect engagement,
perceived/actual understanding, and trust of XAI systems. Our findings showed
that user characteristics in general influenced these measures very little. We
only found one significant effect: a negative effect of both age and openness
on actual understanding. While there was a significant effect of openness on
trust, the regression model fit was not significant. This suggests that lay
users of XAI might be much more similar to each other than they are different.
We now discuss the implications of our work in light of the limitations of our
study, previous research, and lessons for future research.

###  5.1. Limitations

We acknowledge that there are several aspects that could have been improved in
our study. First, the sample size was relatively limited, and despite our
power calculations, our statistical tests may not detect very small effects.

Second, there may have been confounds that introduced noise into our study
data, due to using an online study setup. For example, we had no control over
the environment in which participants completed the study, their dedication to
completing their study, their real engagement with the explanations, etc.

Third, our study focused specifically on explanations provided for a toxicity
classifier. While this is an interesting domain that nonetheless can be easily
understood by lay users, previous work (Buçinca et al., 2020) has already
pointed out problems with using proxy tasks as compared to real tasks. This
might limit the confidence with which results can be taken up. We suggest that
the repeatability of our results should be investigated in real decision-
making tasks and perhaps high-impact domains.

Fourth, our research focused primarily on correlations and statistical testing
using multiple regression models due to the specific nature of our research
questions. However, this methodological approach diverged from the analysis
methods employed in previous studies (e.g., (Millecamp et al., 2020; Conati et
al., 2021)) investigating the impact of personality on XAI in various
applications such as music recommendation and tutoring systems. As a
consequence, a direct comparison of our results with these studies was
impossible. Our choice of analysis methodology was influenced partly by the
size of the available data and the limited variables that could be controlled
in our study. In future investigations encompassing a broader range of
variables and larger-scale data, we intend to move beyond correlation and
regressions and perform more nuanced analysis (e.g. causal analysis (Pearl,
2009)).

Finally, our study did not differentiate between types of explanation content
or presentation. Hence, we can only confirm the impact of user characteristics
on explanations _as a whole_ , not reactions to specific components of
explanations. Similarly, we only evaluated one type of explanation generation
approach, LIME (Ribeiro et al., 2016), and thus cannot speak to the effects of
utilizing other explanations approaches, e.g. SHAP (Lundberg and Lee, 2017) or
Integrated Gradients (IG) (Sundararajan et al., 2017). In addition, our study
only examined one type of explanation, i.e. showing word importance, while
other explanation techniques were not investigated (e.g. example-based
explanations (Sokol and Flach, 2018)).

###  5.2. Previous Work on User Characteristics

Previous work investigating the perception and use of AI and XAI has suggested
that user characteristics such as gender, age, previous experience as well as
personality traits could be used to personalize experience, e.g. (Dhelim et
al., 2022; Tintarev and Masthoff, 2007; Reeder et al., 2023). Our research
used this related work as a launchpad to address the impact of user
characteristics on interacting with explanations of AI, using standard
statistical methods as tools. Our findings problematize using user
characteristics for personalizing explanations. Specifically, our results
indicate that lay users of XAI systems may be more alike than different from
one another, corroborating other work that cautions against the use of gender
as a factor in preferences for explanations (Tran et al., 2023).

This raises an important question that both HCI and XAI researchers and
practitioners must address: _Is personalization an effective strategy for AI
explanations?_ In light of our findings, we endeavor to seek answers to this
question, and argue that, as a community, we need to (re)think whether
personalization for AI explanations may lead to a rabbit hole. Drawing from
personalization research in other domains, we posit that researchers in XAI
might be subject to the same issues due to: _a)_ divergence from fields that
study users and their characteristics; _b)_ the ease of obtaining user
characteristics; _c)_ the conceptual ease of applying user characteristics;
_d)_ precedence, _e)_ authority approval. First, other fields have extensively
studied human-human relationships and the effects of, for example, personality
on trust (Hancock et al., 2023). Takami et al. (2023) demonstrated that
personality-based explanations for e-learning recommender systems were more
effective than conventional explanations. However, much of this research and
changes to theories and implications never make it back to the research in
XAI. Second, it is easy to obtain user characteristics such as age, gender,
expertise, and even personality; in fact, most of these are supplied by
participant recruitment or crowd-sourcing platforms or can be captured easily
in user studies. It is often a matter of “we have the data so might as well
use it”. However, these user characteristics might not be exhaustive. Previous
literature suggested that people rarely engage analytically with each
individual AI recommendation and explanation, and instead develop general
heuristics about whether and when to follow the AI suggestions (Buçinca et
al., 2021); observations that require to tap into people’s cognitive abilities
(e.g., by capturing it through the Need for Cognition, which is a stable
personality trait that captures one’s motivation to engage in effortful mental
activities (Cacioppo and Petty, 1982)). Third, interactions with XAI, as with
any technology, are complex and thus its success is not guaranteed. Thus, we
might look for an easy answer to account for why a system did better than
another, and thus turn to user characteristics as a cause for differences in
user behavior or acceptance. In fact, explanations could inadvertently foster
a false sense of security by obscuring the inherent uncertainty of the
underlying models. Previous literature suggests a need to (re)consider the
target audience when designing XAI (Ehsan et al., 2021a), recognizing that
individuals with different roles and levels of expertise might interpret
explanations in varied ways (Jiang et al., 2022). Fourth, work builds each
other; one paper investigating a user characteristic can spawn papers on the
same or other user characteristics. Looking back there is a long history of
work in HCI on user characteristics and modeling users. Finally, there is also
the matter of authority approval. For example, Gunning et al. (Gunning et al.,
2019) suggest that explanations should be targeted to the user which might
imply that a green light is given to investigate user characteristics in XAI.

###  5.3. Future Research Challenges

Our work points the way to five challenges that need to be addressed in future
research in this area.

####  5.3.1. Repeatability

So far, there have been many studies that have investigated some aspects of
user characteristics and their effects on explanation use, e.g. (Reeder et
al., 2023; Tran et al., 2023; Szymanski et al., 2021). However, the majority
of these pertinent existing studies have not shared the data or code for
analysis. While we did not find any strong patterns in our study, there is a
need for confirming our results. We, therefore, make our framework –
prototype, code, measures, and data – available for researchers to replicate
our study, and we encourage the XAI research community to do the same to
ensure a growing body of evidence that can be systematically reviewed.
However, further steps need to be taken to ensure the viability of these
investigations, as demonstrated by the next two points.

####  5.3.2. Measuring user characteristics and XAI

Recall that we used TIPI to measure personality traits. As pointed out in
section 3.4, TIPI is generally comparable with other, longer forms of
personality trait instruments but struggles to measure openness robustly. This
might explain why we got the results we did: we found that individuals with
higher levels of openness had lower levels of trust in XAI systems, a very
counter-intuitive result. We believe that this might be due to _measuring
errors_ , where we possibly measure something else in the users’
characteristics entirely. Similarly, we used a common, very granular measure
for trust (without accounting for any baseline trusting attitude) and extended
previous approaches using ‘mental model soundness’ (Kulesza et al., 2012) for
measuring actual understanding. Until measuring both user characteristics (as
independent variables) and effects (as dependent variables) are standardized
within our community, or until others have employed our code and protocol to
replicate our measurements, we will not be able to compare results across
studies or come to solid conclusions that help us shape theory for the use and
design of XAI systems, whether to contemplate personalized XAI based on user
characteristics or not.

####  5.3.3. Characterization of users

Our results considered a limited subset of user characteristics, such as age,
gender, previous experience with AI, and personality traits. Recent work has
also suggested that other user characteristics might matter in how people
respond to explanations. For example, _need for cognition_ as a personality
trait has been investigated as to its role in explanations (Buçinca et al.,
2021; Millecamp et al., 2019, 2020) and there are some promising results in
this direction. Other user characteristics beyond gender, age, or previous
experience might also factor into how explanations are perceived or used. For
example, _information processing style_ which has been linked to gender
(Burnett et al., 2016) might influence how explanations are interpreted. It
might also be that _graph interpretation competence_ (Glazer, 2011) can play a
role, especially given the visual nature of many explanation approaches.
Future work is warranted to uncover other user characteristics that remain
stable and provide robust results.

####  5.3.4. Personalization in practice

Our results suggest that incorporating users’ age and, maybe, openness (but
see our previous discussion about the limitations of this dimension of
personality), into the design and implementation of XAI systems could be
beneficial for increasing individuals’ understanding of these systems. This
raises a practical question of how to do that. Are we expecting users to fill
in a possibly lengthy questionnaire every time they use an XAI system?
Building a user profile only seems worthwhile for the sustained use of a
system that provides explanations, where the benefit of personalized design
outweighs the initial cost of providing information. Currently, there are very
few XAI systems targeted at lay users that are to be used repeatedly. In
addition, we still do not know how to design explanations to take user
characteristics into account. For example, what designs targeted at older
users might increase their understanding? Further research into specific
components of explanations might yield some answers in the future.

####  5.3.5. The value of good interaction design

Finally, maybe we are going about this in the wrong way entirely, at this
point in the research landscape. In our study, we focused on a relatively
static presentation of explanations (highlighting word importance) for all
users, minimizing the necessity for extensive user interaction. We do know
that different stakeholders might have diverging needs of explanations that a
system provides and that the content might need to differ in response.
Similarly, we might need to adjust the presentation of explanations in light
of stakeholders’ needs. It has been suggested (Gunning et al., 2019) that
these stakeholders could be segmented into user groups (e.g., data scientists,
domain experts, regulators/auditors), and that these should be considered in
the explanation design. We argue that at this stage we are jumping ahead too
far, without having achieved a solid grounding for addressing the requirements
of these larger user groups. We encourage other researchers to investigate
differences between stakeholder groups in order to build better, more targeted
XAI systems.

##  6\. Reproducibility

To allow for reproducibility, we made our data and code publicly available:
https://github.com/RobertNimmo26/Toxic-Comments-XAI-Study

##  7\. Author Positionality Statement

It is important to acknowledge that our backgrounds and experiences may have
shaped our positionality (Frluckaj et al., 2022; Havens et al., 2020). Within
the context of this work, we situate ourselves in the United Kingdom during
the 21st century, writing as authors primarily engaged in academic and
industry research. Our research team comprises one female and four male
individuals originating from the United Kingdom, Southern Europe, and East
Asia, with diverse ethnic and religious backgrounds. Our collective expertise
spans various fields, including artificial intelligence, explainable AI,
human-computer interaction, computational social science, ubiquitous
computing, and software engineering.

##  8\. Conclusion

In this paper, we explored the impact of user characteristics on XAI. We ran
an online study where participants checked a toxicity classifier that provided
explanations and then examined the relationship between user characteristics
of gender, age, previous experience, and personality on engagement, perceived
and actual understanding, and trust. We found that the users’ characteristics
we collected during the study did not have a significant effect on most
outcome measures. Furthermore, the only significant effect was a negative
relationship between both age and openness to participants’ actual
understanding of the XAI system. Overall, our work sheds light on employing
user characteristics of XAI research, and our discussion problematizes the
direction of future research in this area. Our study is evidence that lay
users may be more similar to each other than different and that XAI designs
should possibly concentrate on more marked differences between larger user
groups.

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