The "Who", "What", and "How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools (2502.13294v2)

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.

• The paper finds that over 220 AI governance tools are unevenly distributed across stakeholder roles and AI lifecycle stages.
• It uses a systematic review and meta-analysis to reveal that only 36.6% of tools have undergone validation.
• The findings emphasize the need for a holistic framework that integrates empirical validation and diverse stakeholder inputs.

A Systematic Review and Meta-Analysis of Actor-Specific Tools in Responsible AI Governance

Introduction

The paper "The 'Who', 'What', and 'How' of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools" presents a comprehensive analysis of tools designed for responsible AI governance across various stakeholders and lifecycle stages. This paper identifies imbalances in tool availability and highlights significant gaps in existing responsible AI governance research and practice. Furthermore, the paper provides a scaffold for future exploration in designing and deploying responsible AI systems, considering the entire AI lifecycle and relevant stakeholders.

Methodology

The authors undertook a rigorous systematic review and meta-analysis of over 220 tools, categorizing them by the roles they address (e.g., designers, developers, leaders) and the stages of the AI lifecycle they pertain to (e.g., data collection, modeling, deployment). The focus was on understanding how these tools align with specific stakeholder requirements and responsibilities across the AI lifecycle.

Findings

The analysis revealed critical gaps in available tools, particularly for roles beyond designers and developers, such as organizational leaders, deployers, end-users, and impacted communities. The paper found that most tools are concentrated in data-centric and modeling stages, neglecting other critical phases like value proposition and deployment (Figure 1).

Figure 1: The distribution of stages present for validated tools (left) and the co-occurrence of pairs of stages present for validated tools (right).

Additionally, the lack of validation for existing tools was highlighted, with only 36.6% of tools having undergone any form of validation prior to their release. The usability and effectiveness of these tools in achieving responsible AI governance are therefore questionable.

Implications

The paper suggests that the current landscape of responsible AI tools promotes a fragmented approach to governance, which could lead to ineffective or misaligned outcomes. Without holistic, validated tools that span the entirety of the AI lifecycle and involve all necessary stakeholders, the risk of unintended consequences and ethical lapses increases.

The authors emphasize the need for validated, comprehensive tools that align with both organizational goals and ethical standards. They argue that tools should be developed with empirical evidence supporting their usability and effectiveness, as well as considering diverse stakeholder perspectives, to ensure a more robust governance infrastructure.

Recommendations

1. Validation of Tools: It is crucial for existing and new tools to undergo rigorous empirical validation. This will ensure the tools are not only technically sound but also effective in practice.
2. Holistic Governance Approach: Responsible AI governance should be approached comprehensively, considering all stages of the AI lifecycle and fostering collaboration among different stakeholders to address ethical issues collectively.
3. Blueprint Utilization: The use of a stakeholder-stage matrix can serve as a blueprint for organizations to determine which tools are available for different roles and stages, as well as assign responsibilities clearly.

Conclusion

The paper highlights significant gaps in the landscape of responsible AI governance tools and emphasizes the need for a more systematic, validated approach to tool development and deployment. By addressing these gaps, the research paves the way for more accountable, effective, and inclusive AI systems that align with ethical standards and societal expectations.