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

2025-02-18

AI Summary

This paper identifies a core dilemma in organizational responsible AI governance: ambiguous responsibility boundaries across AI lifecycle stages and a lack of role- and stage-appropriate operational tools. Methodologically, the study systematically reviews over 220 responsible AI tools and proposes a novel two-dimensional (Actor, Stage) classification framework, integrating systematic review, meta-analysis, and qualitative coding. It identifies three critical governance gaps: (1) unclear accountability attribution, (2) absence of empirical validation for most tools, and (3) severe coverage imbalance across actors and stages. Results show that >80% of tools target developers during data and modeling phases; tools for leadership, deployers, end users, and stages such as value proposition definition and deployment are virtually absent. Moreover, >90% of tools lack empirical evidence. The study establishes a theoretically grounded, empirically benchmarked framework to advance actor–stage–aligned AI governance tool ecosystems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Constraint Satisfaction and Optimization: Solvers and Tools
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance

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.

Problem

• Addressing ambiguity in AI governance responsibilities
• Identifying gaps in responsible AI tools distribution
• Evaluating effectiveness of tools across AI lifecycle stages

Innovation

• Systematic review of AI tools
• Categorizes tools by lifecycle stages
• Identifies gaps in AI governance