Responsible AI Governance: A Systematic Literature Review
Amna Batool, Didar Zowghi, Muneera Bano
TL;DR
The paper addresses how Responsible AI governance is realized across literature by applying a 3W1H framework to 61 studies, revealing uneven attention to Who, What, and When, with most efforts concentrating on How governance is implemented. It synthesizes frameworks, models, tools, and policies and categorizes governance across five levels (team, organisation, industry, national, international), finding organisational-level solutions are most common. Key findings include that only five studies fully answer all 3W1H questions, healthcare being the primary target domain, and data/system governance being more emphasized than human-centric processes. The study identifies ethical and governance gaps and advocates for clearer stakeholder roles and multi-level coordination to align practice with Responsible AI principles, while proposing future work that includes grey literature to broaden understanding and impact.
Abstract
As artificial intelligence transforms a wide range of sectors and drives innovation, it also introduces complex challenges concerning ethics, transparency, bias, and fairness. The imperative for integrating Responsible AI (RAI) principles within governance frameworks is paramount to mitigate these emerging risks. While there are many solutions for AI governance, significant questions remain about their effectiveness in practice. Addressing this knowledge gap, this paper aims to examine the existing literature on AI Governance. The focus of this study is to analyse the literature to answer key questions: WHO is accountable for AI systems' governance, WHAT elements are being governed, WHEN governance occurs within the AI development life cycle, and HOW it is executed through various mechanisms like frameworks, tools, standards, policies, or models. Employing a systematic literature review methodology, a rigorous search and selection process has been employed. This effort resulted in the identification of 61 relevant articles on the subject of AI Governance. Out of the 61 studies analysed, only 5 provided complete responses to all questions. The findings from this review aid research in formulating more holistic and comprehensive Responsible AI (RAI) governance frameworks. This study highlights important role of AI governance on various levels specially organisational in establishing effective and responsible AI practices. The findings of this study provides a foundational basis for future research and development of comprehensive governance models that align with RAI principles.