To collaborate effectively, humans and AI agents need to trust each other. Communication between teammates is an essential component to achieve this, as it makes the AI system more understandable to humans. However, previous research lacks a focus on ways to build an AI agent's trust in its human teammate and, consequently, on how the AI's beliefs can be communicated to the human. As such, this study explores how real-time visual explanations of the AI agent's trust in its human teammate influence human trust and overall satisfaction. Through a user experiment (n=46) conducted on an Urban Search and Rescue simulation, integrating trust explanations was compared against a baseline containing no such information. Results show a statistically significant increase in both human trust and satisfaction when the explanations are provided, highlighting the need for further exploration into methods of communicating trust.

Human-agent teams (HATs) are becoming more prevalent in our current world, necessitating mutual trust between humans and machines. This trust is split into artificial trust (agents trusting humans) and natural trust (humans trusting agents). Both types must be facilitated for effective teamwork. It is hypothesized that communicating artificial trust effectively helps develop natural trust and overall satisfaction. A visual summary of explanations is proposed to serve as an effective communication method. Summaries allow for in-depth information processing, and visual representations are quicker to interpret. This paper examines the impact of using a visual summary of explanations to communicate the agent’s trust beliefs on the human teammate’s natural trust and overall satisfaction within HATs. An experiment (n=40) was conducted to study this effect. Participants collaborated with an artificial agent during an urban search and rescue operation in a simulated 2D grid-world environment. Results show that the inclusion of a visual summary increases the human teammate’s trust in the agent alongside their overall satisfaction. The paper emphasizes the need for further research with longitudinal studies to measure the long-term effectiveness of communicating artificial trust.

As artificial intelligence (AI) is increasingly integrated into decision-making processes, effective collaboration between humans and AI becomes crucial. This study investigates how textual summaries of changes of artificial agent's mental model affect human trust and overall satisfaction. Using a between-groups experimental design in an Urban Search and Rescue scenario, 56 participants were randomly assigned to either receive or not receive these summaries. Trust and satisfaction were measured through established scales and objective metrics, including the number of actions the human and AI performed together. Results show that providing textual summaries significantly increased both human trust in the AI agent and overall satisfaction. While the Task success rate improved with additional communication, other performance metrics showed no significant differences. This research contributes to understanding effective communication strategies in human-AI teams, highlighting the importance of transparency and justifications from artificial agents. These findings can help the design of collaborative AI systems, enhancing trust and satisfaction in human-AI partnerships.

Trust is essential in human-AI collaboration, influencing efficiency, safety, and overall success. Communication influences trust, and the more effective it is, the greater the resulting trust and overall satisfaction. The research question addressed is: “How does a real-time visual (RTV) communication of the mental model of the agent’s trust affect the human teammate’s trust in the agent and overall satisfaction?”. Utilizing an independent measures design, 44 participants were divided into two groups: one experiencing explicit communication of the robot’s mental model, and a control group without such communication. Participants collaborated with an AI agent in a 2D grid-based Urban Search and Rescue game. The study combined subjective measures, such as surveys and open-ended questions, with objective measures. Findings indicate that real-time visual communication did not significantly enhance trust, but it did improve overall satisfaction.

As technological capabilities progress, there is a growing imperative to enhance collaborative dynamics within human-agent teams. Artificial agents and humans possess capabilities that compensate for each other’s limitations. This paper outlines the effect of communication using real-time textual explanations of the artificial agents’ mental model of its trust in the human teammate. An experiment (n = 40) was conducted by examining the impact of real-time textual explanations of the artificial agents’ trust beliefs. The participants collaborated with an artificial agent during a search and rescue mission in a 2D grid world, where they had to rescue six victims in a 10 minute time frame. The results show that real-time textual explanations did affect the natural trust and satisfaction positively compared to the baseline method.

Negotiation is a challenging process for people, which often results in suboptimal agreements between the negotiating parties. This issue leads to lost benefits that one of the negotiating parties could have obtained. To counteract this drawback artificially intelligent negotiation agents are developed. Their goal is to help negotiating parties increase the overall benefits from a negotiation. However, the main drawback of such agents is that people need to trust the agents in order to use them. In this research paper we investigate whether a negotiating agent with a hardliner negotiating style would be trusted more than a negotiating agent with a conceder negotiation style. We answer this question by conducting an user study, where we measure participants' trust in the two negotiation agents and compare the results. From the results we concluded that the assumption is correct for the sample group that participated in the user study.

This research experiment aimed to investigate the level of trust placed in an AI negotiation assistant paired with a truthful explanation of their negotiation strategy versus an opposite explanation within the Pocket Negotiator platform. A between-user study involving 30 participants was conducted to assess participants’ trust perceptions based on the presentation of different explanations about the negotiation assistant. After receiving an explanation
of the negotiation strategy used by the assistant, participants went through a bilateral negotiation on the Pocket Negotiator platform against a robot after
which they completed a questionnaire to evaluate their trust in the assistant. The results were insignificant (p > 0.05) and therefore no conclusion could be drawn about the difference in the participants’ trust in the assistant with a truthful explanation and the assistant with an opposite explanation.

Negotiations have an essential role in our lives as they help us to find mutually beneficial solutions and resolve conflicts. It leads to effective communication and collaboration between the involved parties. Negotiation among parties has high importance to have an outcome that is suitable for all. In such scenarios, negotiation agents can be used by the involved parties. Negotiation agents have explanations that indicate the behavior that the agent is going to follow in the process. This research paper focuses on trust between humans and agents regarding different explanations of the agents. In the research, the negotiation strategy conceder is used in the negotiation with an opposite and truthful explanation of itself. The results from the experiments are compared with each other using the scores of the participants which are measured through a questionnaire. By investigating the effect of different explanations, the research paper aims to answer the question ”What’s the effect of an agent using a truthful explanation of their conceder negotiation style versus an opposite explanation on how much humans trust the agent to negotiate for them?”. The goal is to achieve a trust connection between humans and agents. The main conclusion and the hypothesis of the research are ”people tend to trust less on an agent that does not do what it states in its explanation.” The results of the experiment, discussion of the results, and the conclusion can be found in sections 4, 5, and 7 respectively

Trust in negotiation agents plays a crucial role in their adoption and utilization. However, there is not enough research on what factors influence it. This paper aims to investigate how different explanations of a negotiation agent’s strategy affect human trust and decision-making. Specifically, it compares the effects of a truthful explanation and a neutral explanation on trust in a conceder negotiation agent. An experiment was conducted involving human participants interacting with a negotiation agent in an imaginary negotiation scenario. The participants were divided into two groups, with one group receiving a truthful explanation of the agent’s strategy, and the other receiving a neutral explanation. Trust levels were assessed through a questionnaire and the results were analyzed using an independent samples t test. No significant difference was found between the two groups, but further research needs to be done before conclusions are drawn.

With the prevalence of artificial intelligence recently, more attention is being drawn towards the collaboration between humans and agents. Across the many fields where such an agent can be employed, we are going to specifically examine the domain of negotiation. A critical part to ensure success in this human-agent collaboration is to establish trust between the user and the agent. When considering negotiations, different strategies can be employed by the negotiation agents, the specific negotiation agent we shall use in our study employs the so called "hardliner negotiation style", which can be thought of as taking an uncompromising stance. When introducing the agent, we will either show a neutral introduction which does not mention the choice of strategy or show the hardliner introduction which does cover the chosen strategy. We are going to investigate how the choice of introduction affects the trust of humans in the negotiation agent. Leading us to the research question: What's the effect of an agent using a truthful explanation of their hardliner negotiation style versus a neutral explanation on how much humans trust and rely on the agent to negotiate for them? A between-subjects study took place to answer the question which has indicated that there is no significant difference (U = 111, p = 0.97 two-tailed, α = 0.05) between the two different introductions.

Human-AI teams require trust to operate efficiently and solve certain tasks like search & rescue. Trustworthiness is measured using the ABI model; Ability, Benevolence and Integrity. This research paper tries to observe the effect a conflicting robot has on the human trustworthiness. The hypothesis we try to test is: “human trustworthiness will decrease when paired with a conflicting AI”. We conduct an experiment with one control group playing with a normal agent and an experiment group paired with the conflicting agent. Using the ABI concepts, we model the human trustworthiness across both groups using in-game observations (objective) and a questionnaire (subjective). When comparing the results from both group we see that the conflicting agent does not decrease the objective trustworthiness, however looking at the questionnaires we observe that the subjective human benevolence and integrity are negatively affected when paired with the conflicting agent.

As technology advances, automated systems become more autonomous which leads to a higher interdependence between machine and human. Much research has been done about trust between humans and trust of humans regarding machines. An interesting question that remains is how the behavior of an agent influences human trustworthiness in a human-agent collaborative setting. The research presented by this paper contributes to the understanding of this area. It investigates a specific behavioral trait using the following hypothesis: friendly behavior of an agent improves human trustworthiness. Here, trustworthiness is broken up in the constructs: ability, benevolence and integrity.

An experiment has been conducted using a collaborative Search and Rescue game. The following behaviors of the participants have been measured:
- Ability: speed and effectiveness;
- Benevolence: communication, willingness to help, agreeableness to advice, responsiveness;
- Integrity: truthfulness.
Furthermore, a likert scale has been used to measure the participants' own perception of their trustworthiness. The experiment is conducted with 20 participants in the control group, where the agent spoke in a neutral manner, and 20 in the experimental group, where the agent instilled empathy, stimulated collaboration, encouraged the participants and was affectionate.

The research has shown a significant improvement in the experimental group only for communication and willingness to help. This gives some indication that a friendly agent only slightly improves the trustworthiness of a human. However, the research has some limitations that might also explain the lack of significant results. Firstly, it is unclear to what extent the measures truly measured the constructs of trustworthiness. Secondly, to create a friendly agent, theories from organizational and social psychology are used, which are mostly focussed on human-human relationships, instead of human-agent relationships. Finally, Some confounding variables may have had an impact, like lag in the game and the participant not properly reading the agent’s messages.

AI systems have the ability to complete tasks with greater precision and speed than humans, which has led to an increase in their usage. These systems are often grouped with humans in order to take advantage of the unique abilities of both the AI and the human. However, to make this cooperation as efficient as possible, there needs to be a mutual trust between humans and AIs. While there has been much research concerning the topic of human trust, there is a lack of work done concerning the trust that an artificial agent has toward its human partner. Given that a human must appear trustworthy to an artificial agent in order for that agent to trust him, and that demanding and offering help are important parts of a collaboration, the following research question has been formulated : How does an artificial agent asking a human for advice or help affect that human's trustworthiness ? To answer this research question, an experiment was conducted through an urban search and rescue game using the MATRX Software. Through this game, participants had to collaborate with a robot partner in order to accomplish the task of finding and rescuing 8 victims and delivering them to the correct drop zone. The participants were divided into a control group, who worked alongside a basic rescue robot, and an experimental group, which had a help-seeker robot as a partner. The help-seeker robot differed from the basic robot in its ability to ask the participant for advice, such as asking which room it should search for victims to rescue. Following the experiment, no significant results indicating a positive or negative effect on human trustworthiness by the help-seeker agent's behaviour were found.

Collaborative AI (CAI) is a fast growing field of study. Cooperation between teams composed of humans and artificial intelligence needs to be principled and founded on reciprocal trust. Modelling the trustworthiness of humans is a difficult task because of the ambiguous nature of its definition as well as the effect of team work dynamic. This research defines and measures human trustworthiness within the context of human-AI collaboration and tests how the artificial intelligence agent’s action of offering help plays a role in it. The experiment is conducted through the MATRX framework, in which a human agent and an AI agent collaborate in order to search and rescue victims within the environment. The ABI trust model is used to determine the sub-components that define trustworthiness, which is ability, benevolence and integrity. Trustworthiness is measured in 2 ways, through objective measures which represent the AI agent’s measure of the human trustworthiness and subjective measures that represent the human’s measure of their own trustworthiness. The results show that help offered by the AI agent, improves the ability and benevolence of the human agent in objective metrics but not integrity. Subjective results show no statistically significant change. The research concludes that the trustworthiness perceived by the AI agent is indeed improved, but does not provide evidence of the same from the perception of the human agent.

The number of collaborations between humans and artificial agents has risen steeply in recent years due to the rapid expansion of AI. Numerous studies in social sciences have already established that trust is a crucial factor in ensuring effective teamwork. While the dynamics of trust in human-human relationships or the effects of human comportment on the artificial agents' trustworthiness have been researched, the changes in human trustworthiness as a result of different AI behaviors were scarcely analysed. Therefore, the latter perspective needs to be thoroughly studied as well. This paper has investigated how an AI directing the human can affect human trustworthiness. So far, the directability has been studied as a notion on its own without being linked to trust or its effects on human-AI relationships. For this research, subjective and objective measurements have been used to assess the effects of an AI directing the human in joint activity. In order to quantify the human trustworthiness and assess the possible changes, an experiment has been carried out where participants played a Search & Rescue game developed in the MATRX software. The human's trustworthiness has been calculated using the ABI model and a questionnaire. The obtained results suggest that there is not a significant change in human trustworthiness as a result of the artificial agent directing the human.

Collaboration in teams composed of both humans and automations has an interdependent nature, which demands calibrated trust among all the teammembers. For building suitable autonomous teammates, we need to study how trust and trustworthiness function in such teams. In particular, automations occasionally fail to do their job, which leads to a decrease in human’s trust. However, research has given contradictory statements about the effects of such a reduction of trust on the human’s trustworthiness, i.e. human’s characteristics that make them more or less reliable to the automation. As such, this study investigates how automation failure in a human-automation teamwork scenario affects the human’s trust in the automation and human’s trustworthiness towards the automation. We present a between-subjects controlled experiment in which the participants perform a simulated task in a 2D grid-world, collaborating with an automation in a “moving-out” scenario. During the experiment, we measure the participants’ trust and trustworthiness regarding the automation both subjectively and objectively. Our results show that automation failure negatively affects the human’s trustworthiness, as well as their trust in and liking of the automation. Learning the effects of automation failure in trust and trustworthiness can contribute to a better understanding of the nature and dynamics of trust in these teams, foreseeing undesirable consequences and improving human-automation teamwork.

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.

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.

Understanding trust in human-agent teams is of utmost importance if we want to ensure an efficient and effective collaboration. It is well known that predictability is a core component of trust, however it is still unclear what kind of information an agent should share in order to be perceived as predictable. Here we show that in a simple world setup with a noncomplicated task, there is no significant difference in the measured predictability between agents sharing information pertaining to only world knowledge, actions, world knowledge and actions or world knowledge, actions and explanations. However, previous experience with the framework used or having a technical background do greatly impact the perceived predictability. The small sample size and the data not being representative lead us to conclude that the study should be repeated with a larger and more diverse group of participants and a more complex world setup.