As human-agent collaboration grows increasingly prevalent, it is crucial to understand and enhance the interaction between humans and AI systems. Explainable AI is
fundamental to this interaction, which involves agents conveying essential information to humans for decision-making. This paper investigates how adaptive explanations affect human supervision and trust in robotic systems. The study included 40 participants and compared baseline (non-adaptive) explanations with adaptive explanations. The results showed no significant difference between the two types of explanations; making explanations more abstract did not necessarily improve human supervision or increase trust in robots.

As artificially intelligent agents become integrated into various sectors, they require
an analysis of their capacity to make moral decisions and of the influence of human
supervision on their performance. This study investigates the impact of textual feature
explanations on human supervision and the trust in a semi-autonomous firefighting
robot named Brutus, which operates in a morally complex environment. Grounded in
the field of Explainable AI (XAI), which seeks to render AI decisions transparent, this
research compares textual and visual explanations’ effectiveness in conveying situational
sensitivity during a simulated rescue operation. Through a detailed experimental setup
using the MATRX software to simulate a burning office building, participants’ trust
and understanding were assessed based on their interaction with Brutus using either
textual or visual explanations. This study contributes to the broader discourse on
AI ethics and the optimization of human-agent teaming in high-stakes scenarios. The
findings suggest that textual explanations can enhance human supervision and trust,
fostering greater engagement and satisfaction compared to visual explanations.

With the rise of AI presence in various contexts and spheres of life, ensuring effective
human-AI collaboration, especially in critical domains, is of utmost importance.
Explanations given by AI agent can be of great assistance for this purpose. This study
investigates the impact of global explanations, explaining general allocation rules, on
human supervision and trust in AI within the critical domain of a firefighting scenario,
where human and AI agent have to collaborate to save victims. To this end, a user
study involving 40 participants was performed. The user study compared the baseline
and global explanation scenario and the participants’ trust in the AI and explanation
satisfaction were measured. The results indicated no significant differences between
the two types of explanations, and in fact both achieved similar satisfactory outcomes.
This suggests comparable effectiveness of global explanations in enhancing human-AI
collaboration. Essentially, the insights of this study underscore the need for further
exploration into contextual factors influencing the impact of global explanations and
contribute to designing better human-AI teaming systems in dynamic and ethically
sensitive environments.

The integration of robots in human-robot teams, particularly in high-stakes environments like firefighting, requires effective communication and decision-making to ensure safety and efficiency. This study explores the impact of adding contrastive explanations to feature attributions in robot explanations on human-robot teamwork during firefighting simulations. Contrastive explanations aim to improve human understanding by highlighting why a robot chose one decision over another using allocations of variables. The experiment involved 40 participants, divided into two groups, each interacting with either the baseline or contrastive version of the robot in the simulated environment. Results indicate that contrastive explanations significantly increased participants' capacity trust in the robot, though they did not significantly affect moral trust. Additionally, the results showed a lower satisfaction level with the explanations given by the robot. The disagreement rate between human decisions and robot actions was lower in the contrastive group, suggesting possible enhanced understanding and agreement with the robot's decisions. These findings underscore the potential of contrastive explanations to enhance trust and collaboration in human-robot teams, paving the way for more effective integration of robots in critical operations. Future research should focus on larger sample sizes and explore the inclusion of contrastive decisions made by the robot alongside explanations to further validate these findings.

In human-AI agent interactions, providing clear visual or textual explanations for the agent's actions and decisions is crucial for ensuring successful collaboration. This research investigates whether having the visual explanations displayed only on-demand, instead of having them consistently shown as the baseline, has an impact on the human supervisor's level of confidence and satisfaction with the AI agent. Therefore, a case study of 40 participants was conducted to explore this hypothesis and the participants were divided into 2 groups, one interacting with the on-demand condition, and the other with the baseline one. Through questionnaires, the participants' capacity and moral trust in the robot, the explainable artificial intelligence satisfaction, and the disagreement rate with the robot's decisions have been collected. Demographic data was gathered from the participants to explore whether their background could impact the collaboration. This data included the participants' gender, age, education, gaming experience, risk propensity, trust propensity, and utilitarianism. The resulting statistical analyses indicated no significant differences between the baseline and the on-demand conditions concerning trust and explanation satisfaction. This suggests that the overall collaboration was not primarily impacted by the frequency of visual explanations requested on demand. Although the results implied a high satisfaction with the interaction, further studies with more diverse user groups are recommended. Overall, this research reinforces the importance of transparency in decision-making processes during the collaboration between an AI agent and a human supervisor.

Explainable AI (XAI) has gained increasing attention from more and more researchers with an aim to improve human interaction with AI systems. In the context of human-agent teamwork (HAT), providing explainability to the agent helps to increase shared team knowledge and belief, therefore improving overall teamwork. With various backgrounds and characteristics of humans, expert video gamers are found to have better perception and cognitive ability. This study aims to study the effect of information amount in explanations on four factors: subjective workload, teamwork performance, trust, and explanation satisfaction in different expertise levels in human-agent teamwork. To investigate the research question, we designed a simulated search and rescue task, encompassing two types of explanations: the one containing less detailed information, and the other presenting more detailed information. After conducting the experiment with 42 participants, we first divided all participants into three expertise levels based on their self-reported game frequency and the mock task score in the tutorial. Then we statistically analyzed the effect of information amount and expertise levels on the subjective workload, team performance, trust, explanation satisfaction, and activity level. In conclusion, we did not find evidence that adapting the information amount in explanations to gaming expertise levels can yield an improvement in the user experience during simulated search and rescue tasks. However, subjective workload is found to have a negative effect on explanation satisfaction. For future studies, it may be worth investigating whether expert gamers require explanations with very detailed information in HAT.

Aligning human trust to correspond with an agent's trustworthiness is an essential collaborative element within Human-Agent Teaming (HAT). Misalignment of trust could cause sub-optimal usage of the agent. Trust can be influenced by providing explanations which clarify the agent's actions. However, research often approaches explanations statically, making them not adjustable to real-time situations. In this research, we study the effectiveness of an agent capable of modelling human trust and tailoring explanations to influence it. We achieve this by modifying an existing HAT environment and setting up an experiment comparing a trust and baseline agent. Modelling human trust is calculated through the number of suggestions ignored. When the model estimates low trust, more explanation types are used during communication. Higher trust uses fewer explanation types in order to save time. However, the results indicate no difference between the baseline and trust agent rejecting the hypothesis. A potential cause for the rejection can be found in either a flaw in the agents' design or information overload.

Communication is one of the main challenges in Human-Agent Teams (HATs). An important aspect of communication in HATs is the use of explanation styles. This thesis examines the influence of an explainable agent adapting its explanation style to a supervising human team leader on team performance, trust, situation awareness, collaborative fluency, explanation satisfaction, understandability, and user-awareness. To perform a simulated Search And Rescue (SAR) task, a HAT is designed. With this design, a pre-study is then conducted using a questionnaire to discover the best-ranked explanation styles in the most important situations of the SAR task. Next, the user-study is carried out with 46 participants, using the HAT design and analysed data from the pre-study. There are two conditions: the agent adapting the explanation style to the human team leader and the baseline condition where the explanation style is randomised. The results show that the subjective measurements of trust, understandability, explanation satisfaction, and perceived user-awareness are significantly higher in the adaptive agent condition group. The same cannot be concluded for objective measurements such as team performance and situation awareness.

Mutual predictability shows itself as a contributing factor to mutual trust and is known to improve the effectiveness in a human-agent teamwork setting. As team members communicate to coordinate the team through the task, the question arises as to what information the human should share to be predictable to an agent. To experiment with measuring the predictability, defined as to what extent the agent can anticipate the actions of the human, we used the Blocks World for Teams (BW4T) task. The two information types shared are intentions and world knowledge. The predictability is evaluated in the background by the agent logging, with automatic help from the human agent, a sequence containing the human performed actions. The agent can assign 2 probabilities to a human performed action. The first indicates with what probability the agent could say the human chose this action. The second indicates with what probability the agent could say this action led to this outcome. These probabilities then vary based on whether the information sent beforehand implied this action. The experiment has 4 different cases: sharing no information, sharing intentions, sharing world knowledge, and sharing both types. It is shown that sharing intentions contributes the most to higher predictability, due to these messages being the most effective at implying the future action of the human. World knowledge and sharing both types have less effect on predictability. We speculate that this is because of the larger amount of messages to share as well as when to share them, which overloads the human.

The collaboration between AI agents (Artificial Intelligence) and human is an essential part of achieving complex goals more efficiently. Many aspects are influential in achieving effective teamwork. One of them is trust. In addition, sharing the mental model would improve the understanding of the other’s behavior and the prediction of their actions. In this paper, we will analyze the influence of sharing the mental model on team performance. We will consider the human side’s trust in an AI agent under various shared mental model structures.