Recent research claims that information cues and system attributes of algorithmic decision-making processes affect decision subjects’ fairness perceptions. However, little is still known about how these factors interact. This paper presents a user study (N = 267) investigating the individual and combined effects of explanations, human oversight, and contestability on informational and procedural fairness perceptions for high- and low-stakes decisions in a loan approval scenario. We find that explanations and contestability contribute to informational and procedural fairness perceptions, respectively, but we find no evidence for an effect of human oversight. Our results further show that both informational and procedural fairness perceptions contribute positively to overall fairness perceptions but we do not find an interaction effect between them. A qualitative analysis exposes tensions between information overload and understanding, human involvement and timely decision-making, and accounting for personal circumstances while maintaining procedural consistency. Our results have important design implications for algorithmic decision-making processes that meet decision subjects’ standards of justice.

Selecting reviewers for code changes is a critical step for an efficient code review process. Recent studies propose automated reviewer recommendation algorithms to support developers in this task. However, the evaluation of recommendation algorithms, when done apart from their target systems and users (i.e., code review tools and change authors), leaves out important aspects: perception of recommendations, influence of recommendations on human choices, and their effect on user experience. This study is the first to evaluate a reviewer recommender in vivo. We compare historical reviewers and recommendations for over 21,000 code reviews performed with a deployed recommender in a company environment and set out to measure the influence of recommendations on users' choices, along with other performance metrics. Having found no evidence of influence, we turn to the users of the recommender. Through interviews and a survey we find that, though perceived as relevant, reviewer recommendations rarely provide additional value for the respondents. We confirm this finding with a larger study at another company. The confirmation of this finding brings up a case for more user-centric approaches to designing and evaluating the recommenders. Finally, we investigate information needs of developers during reviewer selection and discuss promising directions for the next generation of reviewer recommendation tools. Preprint: https://doi.org/10.5281/zenodo.1404814.

Existing eXplainable Artificial Intelligence (XAI) techniques support people in interpreting AI advice. However, although previous work evaluates the users’ understanding of explanations, factors influencing the decision support are largely overlooked in the literature. This article addresses this gap by studying the impact of user uncertainty , AI correctness , and the interaction between AI uncertainty and explanation logic-styles for classification tasks. We conducted two separate studies: one requesting participants to recognize handwritten digits and one to classify the sentiment of reviews. To assess the decision making, we analyzed the task performance, agreement with the AI suggestion, and the user’s reliance on the XAI interface elements. Participants make their decision relying on three pieces of information in the XAI interface (image or text instance, AI prediction, and explanation). Participants were shown one explanation style (between-participants design) according to three styles of logical reasoning (inductive, deductive, and abductive). This allowed us to study how different levels of AI uncertainty influence the effectiveness of different explanation styles. The results show that user uncertainty and AI correctness on predictions significantly affected users’ classification decisions considering the analyzed metrics. In both domains (images and text), users relied mainly on the instance to decide. Users were usually overconfident about their choices, and this evidence was more pronounced for text. Furthermore, the inductive style explanations led to overreliance on the AI advice in both domains—it was the most persuasive, even when the AI was incorrect. The abductive and deductive styles have complex effects depending on the domain and the AI uncertainty levels.

A common criteria for Explainable AI (XAI) is to support users in establishing appropriate trust in the AI – rejecting advice when it is incorrect, and accepting advice when it is correct. Previous findings suggest that explanations can cause an over-reliance on AI (overly accepting advice). Explanations that evoke appropriate trust are even more challenging for decision-making tasks that are difficult for humans and AI. For this reason, we study decision-making by non-experts in the high-uncertainty domain of stock trading. We compare the effectiveness of three different explanation styles (influenced by inductive, abductive, and deductive reasoning) and the role of AI confidence in terms of a) the users’ reliance on the XAI interface elements (charts with indicators, AI prediction, explanation), b) the correctness of the decision (task performance), and c) the agreement with the AI’s prediction. In contrast to previous work, we look at interactions between different aspects of decision-making, including AI correctness, and the combined effects of AI confidence and explanations styles. Our results show that specific explanation styles (abductive and deductive) improve the user’s task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user’s decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI. Our findings further indicate a need to consider AI confidence as a criterion for including or excluding explanations from AI interfaces. In addition, this paper highlights the importance of carefully selecting an explanation style according to the characteristics of the task and data.

Psychological factors such aspersonality, emotions, social connections, anddecision biasescan significantly affect the outcome of a decision process. These factors are also prevalent in the existing literature related to the inclusion of psychological aspects in recommender system development.Personalityandemotionsof users have strong connections with their interests and decision-making behavior. Hence, integrating these factors into recommender systems can help to better predict users’ item preferences and increase the satisfaction with recommended items. In scenarios where decisions are made by groups (e.g., selecting a tourism destination to visit with friends),group compositionandsocial connectionsamong group members can affect the outcome of a group decision.Decision biasesoften occur in a recommendation process, since users usually apply heuristics when making a decision. These biases can result in low-quality decisions. In this article, we provide a rigorous review of existing research on the influence of the mentioned psychological factors on recommender systems. These factors are not only considered in single-user recommendation scenarios but, importantly, also in group recommendation ones, where groups of users are involved in a decision-making process. We include working examples to provide a deeper understanding of how to take into account these factors in recommendation processes. The provided examples go beyond single-user recommendation scenarios by also considering specific aspects of group recommendation settings.