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Situated Accountability: Ethical Principles, Certification Standards, and Explanation Methods in Applied AI

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

DOI

Artificial intelligence (AI) has the potential to benefit humans and society by its employment in important sectors. However, the risks of negative consequences have underscored the importance of accountability for AI systems, their outcomes, and the users of such systems. In recent years, various accountability mechanisms have been put forward in pursuit of the responsible design, development, and use of AI. In this article, we provide an in-depth study of three such mechanisms as we analyze Scandinavian AI developers' encounter with (1) ethical principles, (2) certification standards, and (3) explanation methods. By doing so, we contribute to closing a gap in the literature between discussions of accountability on the research and policy level, and accountability as a responsibility put on the shoulders of developers in practice. Our study illustrates important flaws in the current enactment of accountability as an ethical and social value which, if left unchecked, risks undermining the pursuit of responsible AI. By bringing attention to these flaws, the article signals where further work is needed in order to build effective accountability systems for AI.

Original languageEnglish
Title of host publicationAIES '21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
Number of pages12
Place of publicationNew York
PublisherAssociation for Computing Machinery
Publication year2021
Pages574-585
ISBN (Electronic)978-1-4503-8473-5
DOIs
Publication statusPublished - 2021
EventAAAI / ACM Conference on Artificial Intelligence, Ethics, and Society - AIES 2021 - Virtual
Duration: 19 May 202121 May 2021
Conference number: 4

Conference

ConferenceAAAI / ACM Conference on Artificial Intelligence, Ethics, and Society - AIES 2021
Nummer4
ByVirtual
Periode19/05/202121/05/2021

    Research areas

  • AI, Machine learning, Algorithmic systems, Accountability, Responsible AI, AI ethics, Certification, Explainable AI, Case Study, Ethnography, DECISION-MAKING, SOFT LAW, MACHINE, CLASSIFICATION, DESIGN, VIRTUE

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