The "Who", "What", and "How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools

Abstract

The implementation of responsible AI in an organization is inherently complex due to the involvement of multiple stakeholders, each with their unique set of goals and responsibilities across the entire AI lifecycle. These responsibilities are often ambiguously defined and assigned, leading to confusion, miscommunication, and inefficiencies. Even when responsibilities are clearly defined and assigned to specific roles, the corresponding AI actors lack effective tools to support their execution. Toward closing these gaps, we present a systematic review and comprehensive meta-analysis of the current state of responsible AI tools, focusing on their alignment with specific stakeholder roles and their responsibilities in various AI lifecycle stages. We categorize over 220 tools according to AI actors and stages they address. Our findings reveal significant imbalances across the stakeholder roles and lifecycle stages addressed. The vast majority of available tools have been created to support AI designers and developers specifically during data-centric and statistical modeling stages while neglecting other roles such as institutional leadership, deployers, end-users, and impacted communities, and stages such as value proposition and deployment. The uneven distribution we describe here highlights critical gaps that currently exist in responsible AI governance research and practice. Our analysis reveals that despite the myriad of frameworks and tools for responsible AI, it remains unclear \emph{who} within an organization and \emph{when} in the AI lifecycle a tool applies. Furthermore, existing tools are rarely validated, leaving critical gaps in their usability and effectiveness. These gaps provide a starting point for researchers and practitioners to create more effective and holistic approaches to responsible AI development and governance.

Accepted to ACM Conference on Fairness, Accountability, and Transparency 2025. 15 pages, 3 figures

References in corpus (13)

• The Fallacy of AI Functionality
• Problem Formulation and Fairness
• Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification
• Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning
• Envisioning Communities: A Participatory Approach Towards AI for Social Good
• Discovering and Validating AI Errors With Crowdsourced Failure Reports
• `It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values
• The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals
• LiFT: A Scalable Framework for Measuring Fairness in ML Applications
• A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations
• Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
• A Maturity Assessment Framework for Conversational AI Development Platforms
• Prescriptive and Descriptive Approaches to Machine-Learning Transparency