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

TY - GEN

T1 - Situated Accountability

T2 - AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society - AIES 2021

AU - Henriksen, Anne

AU - Enni, Simon

AU - Bechmann, Anja

N1 - Conference code: 4

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - AI

KW - Machine learning

KW - Algorithmic systems

KW - Accountability

KW - Responsible AI

KW - AI ethics

KW - Certification

KW - Explainable AI

KW - Case Study

KW - Ethnography

KW - DECISION-MAKING

KW - SOFT LAW

KW - MACHINE

KW - CLASSIFICATION

KW - DESIGN

KW - VIRTUE

UR - http://www.scopus.com/inward/record.url?scp=85112462721&partnerID=8YFLogxK

U2 - 10.1145/3461702.3462564

DO - 10.1145/3461702.3462564

M3 - Article in proceedings

SP - 574

EP - 585

BT - AIES '21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

PB - Association for Computing Machinery

CY - New York

Y2 - 19 May 2021 through 21 May 2021

ER -