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, Veas Eduardo, Kern Roman, Nad Tomislav, Kopeinik Simone
Know Center Research GmbH, Graz, Austria.
Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria.
Front Big Data. 2024 Nov 29;7:1467222. doi: 10.3389/fdata.2024.1467222. eCollection 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.
人工智能（AI）技术正在（重新）塑造现代生活，推动着众多领域的创新。然而，一些人工智能系统产生了意想不到的或不良的结果，或者被以可疑的方式使用。因此，关于人工智能系统要被视为可信必须满足的各个方面，公众和学术讨论激增。在本文中，我们沿着六个要求综合了可信人工智能的现有概念：（1）人类能动性与监督，（2）公平与非歧视，（3）透明度与可解释性，（4）稳健性与准确性，（5）隐私与安全，以及（6）问责制。对于每一项要求，我们给出定义，描述如何建立和评估它，并讨论特定要求的研究挑战。最后，我们通过确定跨这些要求在以下方面的总体研究挑战来结束此分析：（1）跨学科研究，（2）概念清晰度，（3）上下文依赖性，（4）不断演进的系统中的动态变化，以及（5）在现实世界背景下的调查。因此，本文综合并巩固了目前在各个学术子领域和公共论坛中广泛且活跃的讨论。它旨在为广大读者提供参考，并作为未来研究方向的基础。
