Strategies for improving the explainability of artificial agents are a key approach to support the understandability of artificial agents’ decision-making processes and their trustworthiness. However, since explanations are not inclined to standardization, finding solutions that fit the algorithmic-based decision-making processes of artificial agents poses a compelling challenge. This paper addresses the concept of trust in relation to complementary aspects that play a role in interpersonal and human–agent relationships, such as users’ confidence and their perception of artificial agents’ reliability. Particularly, this paper focuses on non-expert users’ perspectives, since users with little technical knowledge are likely to benefit the most from “post-hoc”, everyday explanations. Drawing upon the explainable AI and social sciences literature, this paper investigates how artificial agent’s explainability and trust are interrelated at different stages of an interaction. Specifically, the possibility of implementing explainability as a trust building, trust maintenance and restoration strategy is investigated. To this extent, the paper identifies and discusses the intrinsic limits and fundamental features of explanations, such as structural qualities and communication strategies. Accordingly, this paper contributes to the debate by providing recommendations on how to maximize the effectiveness of explanations for supporting non-expert users’ understanding and trust.

Negotiation processes involve a substantive, a communication, and an emotional dimension. These dimensions have been analyzed mainly in isolation of each other. We introduce an approach to consider all three dimensions and present an empirical study on the relations between these dimensions. Results indicate a strong linkage between communication behavior and emotions, while connections to the substantive dimension of the negotiation process are weaker.

We develop analytical models to assist negotiators in formulating offers in a concession-based negotiation process. Our approach is based on plausible requirements for offers formulated in terms of utility values for both the negotiator making the offer and the opponent receiving it. These requirements include value creation, reciprocity, and the fact that an offer actually leads to concessions. Trade-offs between their own and the opponent’s utilities can be formulated by negotiators and define a search path in the utility space. Solutions along this search path are then mapped back into the issue space to generate actual offers. We present and discuss several variants of optimization models to generate such offers and illustrate them with an numerical example.

This article reports on a longitudinal experiment in which the influence of an assistive system’s malfunctioning and transparency on trust was examined over a period of seven days. To this end, we simulated the system’s personalized recommendation features to support participants with the task of learning new texts and taking quizzes. Using a 2 × 2 mixed design, the system’s malfunctioning (correct vs. faulty) and transparency (with vs. without explanation) were manipulated as between-subjects variables, whereas exposure time was used as a repeated-measure variable. A combined qualitative and quantitative methodological approach was used to analyze the data from 171 participants. Our results show that participants perceived the system making a faulty recommendation as a trust violation. Additionally, a trend emerged from both the quantitative and qualitative analyses regarding how the availability of explanations (even when not accessed) increased the perception of a trustworthy system.