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Establishing and Evaluating Trustworthy AI: Overview and Research Challenges

Authors :
Kowald, Dominik
Scher, Sebastian
Pammer-Schindler, Viktoria
Müllner, Peter
Waxnegger, Kerstin
Demelius, Lea
Fessl, Angela
Toller, Maximilian
Estrada, Inti Gabriel Mendoza
Simic, Ilija
Sabol, Vedran
Truegler, Andreas
Veas, Eduardo
Kern, Roman
Nad, Tomislav
Kopeinik, Simone
Publication Year :
2024

Abstract

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.<br />Comment: Accepted in Frontiers in Big Data and AI, Research Topic: Towards Fair AI for Trustworthy Artificial Intelligence

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2411.09973
Document Type :
Working Paper