Auditing of AI: Legal, Ethical and Technical Approaches
<|reference_start|>Auditing of AI: Legal, Ethical and Technical Approaches: AI auditing is a rapidly growing field of research and practice. This review article, which doubles as an editorial to Digital Societys topical collection on Auditing of AI, provides an overview of previous work in the field. Three key points emerge from the review. First, contemporary attempts to audit AI systems have much to learn from how audits have historically been structured and conducted in areas like financial accounting, safety engineering and the social sciences. Second, both policymakers and technology providers have an interest in promoting auditing as an AI governance mechanism. Academic researchers can thus fill an important role by studying the feasibility and effectiveness of different AI auditing procedures. Third, AI auditing is an inherently multidisciplinary undertaking, to which substantial contributions have been made by computer scientists and engineers as well as social scientists, philosophers, legal scholars and industry practitioners. Reflecting this diversity of perspectives, different approaches to AI auditing have different affordances and constraints. Specifically, a distinction can be made between technology-oriented audits, which focus on the properties and capabilities of AI systems, and process oriented audits, which focus on technology providers governance structures and quality management systems. The next step in the evolution of auditing as an AI governance mechanism, this article concludes, should be the interlinking of these available (and complementary) approaches into structured and holistic procedures to audit not only how AI systems are designed and used but also how they impact users, societies and the natural environment in applied settings over time.<|reference_end|>
@article{mokander2024auditing,
    title={Auditing of AI: Legal, Ethical and Technical Approaches},
    author={Jakob Mokander},
    journal={DISO 2, 49 (2023)},
    year={2024},
    doi={10.1007/s44206-023-00074-y},
    archivePrefix={arXiv},
    eprint={2407.06235},
    primaryClass={cs.CY cs.AI}
}