Establishing and evaluating trustworthy AI: overview and research challenges - Kowald, Dominik; Scher, Sebastian; Pammer-Schindler, Viktoria; Müllner, Peter; Waxnegger, Kerstin; Demelius, Lea; Fessl, Angela; Toller, Maximilian; Mendoza Estrada, Inti Gabriel; Šimić, Ilija; Sabol, Vedran; Trügler, Andreas; et al. (Frontiers in Big Data, 2024)
Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: (1) human agency and oversight, (2) fairness and non-discrimination, (3) transparency and explainability, (4) robustness and accuracy, (5) privacy and security, and (6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to (1) interdisciplinary research, (2) conceptual clarity, (3) context-dependency, (4) dynamics in evolving systems, and (5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions.