As robots and virtual agents are increasingly envisioned as long-term companions, understanding how trust develops becomes crucial for ensuring safe and appropriate human-robot relationships. This research investigates how affective and cognitive trust evolve in social human-robot interactions. Participants (n=40) engaged in a 2 (social attitude: social, baseline) × 3 (time: t1, t2, t3) mixed-design user study with a social robot, using a novel Card Divination Task developed to elicit both cognitive and affective trust dimensions. Results show that cognitive trust develops early while affective trust emerges gradually. Moreover, social cues enhance both cognitive trust, affective trust, and participants’ certainty in trust judgment. These findings provide empirical support for the theoretical distinction between trust dimensions and highlight the role of social behavior in shaping trust over repeated interactions.

Mutual trust between humans and interactive artificial agents is crucial for effective human-agent teamwork.This involves not only the human appropriately trusting the artificial teammate, but also the artificial teammate assessing the human's trustworthiness for different tasks (i.e., artificial trust in human partners).Literature indicated that transparency and explainability is generally beneficial for human-agent collaboration.However, communicating artificial trust potentially affects human trust and satisfaction, which impact team dynamics.Towards studying these effects, we developed an artificial trust model and implemented five distinct communication approaches which varied in modality (visual/graphical and/or text), level (communication and/or explanation), and timing (real-time or occasional).We evaluated the effects of the different communication styles through a user study (N=120) in a 2D grid-world Search and Rescue scenario.Our results show that all our artificial trust explanations improved human trust and satisfaction, but the mere graphical communication of it did not.These results are bound to the specific scenario and context in which this study was run and require further exploration.As such, this work presents a first step towards understanding the consequences of communicating and explaining to a human teammate their assessed trustworthiness.

Social interactions are prevalent in our lives. These can be observed, e. g., online using social media, however, also offline specifically using sensors. In such contexts, typically time-stamped interactions are recorded, which can also be inferred from real-time location of humans. Such interaction data can then be modeled as so-called social interaction networks. For their analysis, a variety of different approaches can be applied. A prominent research direction is then the detection of patterns describing specific subgroups with exceptional behavioral characteristics, given some measure of interest. In the standard case of plain graphs modeling the interaction networks, methods for identifying such subgroups mainly focus on structural characteristics of the network and/or the induced subgraph. For attributed social networks, then additional attributive information can be exploited. This paper proposes to focus on the dyadic structure of the attributed social interaction networks, thus enabling a compositional perspective for identifying interesting subgroup patterns. Specifically, we can then analyze spatio-temporal data modeled as attributed social interaction networks for identifying exceptional social behavior. The presented approach adapts local pattern mining using subgroup discovery to the dyadic setting, exploiting attribute information of the spatio-temporal attributed interaction networks. With this, specific characteristics of social interactions are considered, i. e., duration and frequency, for identifying subgroups capturing social behavior that deviates from the norm. For subgroup discovery, we propose according interestingness measures in the form of seven novel quality functions and discuss their properties. In our experimentation, we perform an evaluation demonstrating the efficacy of the presented approach using four real-world datasets on face-to-face interactions in academic conferencing as well as school playground contexts. Our results indicate that the proposed method returns interesting, meaningful, and valid findings and results.

In teams composed of humans, we use trust in others to make decisions, such as what to do next, who to help and who to ask for help. When a team member is artificial, they should also be able to assess whether a human teammate is trustworthy for a certain task. We see trustworthiness as the combination of (1) whether someone will do a task and (2) whether they can do it. With building beliefs in trustworthiness as an ultimate goal, we explore which internal factors (krypta) of the human may play a role (e.g., ability, benevolence, and integrity) in determining trustworthiness, according to existing literature. Furthermore, we investigate which observable metrics (manifesta) an agent may take into account as cues for the human teammate’s krypta in an online 2D grid-world experiment ( n = 54). Results suggest that cues of ability, benevolence and integrity influence trustworthiness. However, we observed that trustworthiness is mainly influenced by human’s playing strategy and cost-benefit analysis, which deserves further investigation. This is a first step towards building informed beliefs of human trustworthiness in human-AI teamwork.

Appropriate trust is an important component of the interaction between people and AI systems, in that “inappropriate” trust can cause disuse, misuse, or abuse of AI. To foster appropriate trust in AI, we need to understand how AI systems can elicit appropriate levels of trust from their users. Out of the aspects that influence trust, this article focuses on the effect of showing integrity. In particular, this article presents a study of how different integrity-based explanations made by an AI agent affect the appropriateness of trust of a human in that agent. To explore this, (1) we provide a formal definition to measure appropriate trust, (2) present a between-subject user study with 160 participants who collaborated with an AI agent in such a task. In the study, the AI agent assisted its human partner in estimating calories on a food plate by expressing its integrity through explanations focusing on either honesty, transparency, or fairness. Our results show that (a) an agent who displays its integrity by being explicit about potential biases in data or algorithms achieved appropriate trust more often compared to being honest about capability or transparent about the decision-making process, and (b) subjective trust builds up and recovers better with honesty-like integrity explanations. Our results contribute to the design of agent-based AI systems that guide humans to appropriately trust them, a formal method to measure appropriate trust, and how to support humans in calibrating their trust in AI.

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Abstract Our lives are made of social interactions which can be recorded through personal gadgets as well as sensors capturing ubiquitous and social data. This type of data, such as spatio‐temporal data from the real‐time location of people, for example, can then be used for inferring interactions which can be translated into behavioural patterns. In this paper, we consider the automatic discovery of exceptional social behaviour from spatio‐temporal interaction data, focusing on two areas: exceptional subgroups and spatio‐temporal outliers – both in the form of descriptive patterns. For that, we propose a method for exceptional social behaviour discovery, combining subgroup discovery and network science methods for identifying behaviour that deviates from the norm. We also propose the use of two outlier detection metrics for identifying outliers, namely the Local Outlier Factor (LOF) and the Voronoi area. We applied the proposed method on synthetic data as well as two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data.

Mutual trust is considered a required coordinating mechanism for achieving effective teamwork in human teams. However, it is still a challenge to implement such mechanisms in teams composed by both humans and AI (human-AI teams), even though those are becoming increasingly prevalent. Agents in such teams should not only be trustworthy and promote appropriate trust from the humans, but also know when to trust a human teammate to perform a certain task. In this project, we study trust as a tool for artificial agents to achieve better team work. In particular, we want to build mental models of humans so that agents can understand human trustworthiness in the context of human-AI teamwork, taking into account factors such as human teammates', task's and environment's characteristics.

As intelligent agents are becoming human's teammates, not only do humans need to trust intelligent agents, but an intelligent agent should also be able to form artificial trust, i.e. a belief regarding human's trustworthiness. We see artificial trust as the beliefs of competence and willingness, and we study which internal factors (krypta) of the human may play a role when assessing artificial trust. Furthermore, we investigate which observable measures (manifesta) an agent may take into account as cues for the human teammate's krypta. This paper proposes a conceptual model of artificial trust for a specific task during human-agent teamwork. Our model proposes observable measures related to human trustworthiness (ability, benevolence, integrity) and strategy (perceived cost and benefit) as predictors for willingness and competence, based on literature and a preliminary user study.