Accountability in artificial intelligence: what it is and how it works

By Claudio Novelli, Mariarosaria Taddeo, Luciano Floridi. Ai & Society, 2023

350 citations recorded by the source corpus.

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, and implications). We analyze this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasize or prioritize some over others depending on the proactive or reactive use of accountability and the missions of AI governance.

Subfields: Artificial Intelligence, Safety Research

View Paper: https://www.semanticscholar.org/paper/2c4e1a6853eb92fdf9d4b80db661e83bd69adc6c

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Citations in the corpus

Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation (2023, background): “Accountability is a matter of compliance with ethical and legal standards, answerability, reporting and oversight, and attribution and enforcement of consequences [117].”