Claudio Novelli, Mariarosaria Taddeo, and Luciano Floridi provide a comprehensive analysis of accountability in the context of artificial intelligence (AI). The paper begins by defining accountability as a relation of answerability that requires recognition of authority, interrogation of power, and limitations on that power. The authors then specify the content of this relation through seven features, including context, range, agent, forum, standard, process, and implications. They also identify four goals of accountability in AI, including compliance, report, oversight, and enforcement. The authors apply their analysis to AI governance, highlighting the importance of proactive and reactive accountability and the governance missions that underlie different accountability policies. The paper concludes with reflections on the challenges and opportunities for accountability in the context of AI.
The authors’ analysis of accountability in AI is both detailed and nuanced. They provide a clear and comprehensive framework for understanding the different dimensions of accountability and the goals that it can serve. The paper’s focus on the importance of both proactive and reactive accountability is particularly important, as it highlights the need for accountability to be built into the design, development, and deployment of AI systems, rather than being an afterthought. The authors’ emphasis on the importance of governance objectives is also useful, as it highlights the need for accountability policies to be tailored to specific contexts and goals.
One of the most interesting aspects of the paper is the authors’ analysis of the relationship between accountability and power. The authors argue that accountability is a necessary mechanism for limiting the power of those who develop and deploy AI systems. This raises broader philosophical questions about the nature of power and its relationship to ethics and morality. For example, how can we ensure that those who hold power are held accountable for their actions? What ethical principles should guide the use of power in the context of AI? These are important questions that require further philosophical exploration.
The authors’ analysis of accountability in AI also raises important questions about the role of technology in society. As AI systems become more prevalent and powerful, the need for accountability becomes ever more pressing. However, ensuring accountability is not a simple matter, as it requires balancing competing values and interests. The authors suggest that future research should focus on developing more concrete and practical guidelines for implementing accountability in the context of AI. This is an important avenue for further exploration, as it could help to ensure that AI systems are developed and deployed in ways that are consistent with ethical and moral principles. Overall, this paper provides a useful framework for understanding accountability in the context of AI, and it offers important insights for both philosophers and policymakers.
Abstract
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.
Accountability in artificial intelligence: what it is and how it works