Responsible Artificial Intelligence (RAI) is a subset of the ethics associated with the use of artificial intelligence, which will only increase with the recent advent of new regulatory frameworks. However, if many firms have announced the establishment of AI governance rules, there is currently an important gap in understanding whether and why these announcements are being implemented or remain “decoupled” from operations. We assess how large global firms have so far implemented RAI, and the antecedents to RAI implementation across a (...) wide range of RAI initiatives. We find that the operationalization of RAI practices is scattered across firms, with only a fringe of companies extensively industrializing RAI. Social pressure pushes RAI design (”saying”) rather than implementation but the reverse is true for competitive pressure. AI capabilities as a bundle of data quality AI architecture, and talents are strongly associated with RAI design to scaling. (shrink)

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. (shrink)