Systematic Review of AI Accountability and Responsibility Frameworks, 2023-2025

A Multivocal Literature Review (MLR) covering academic and grey literature from January 2023 to March 2025 on accountability and responsibility in AI systems. The review, authored by Shi Yun Ng and shared on figshare, compares governance frameworks and examines implementation challenges across industries and society. It provides recommendations for fostering accountability, offering a roadmap for policymakers and researchers.

Use Cases

• Comparing AI governance frameworks for accountability and responsibility based on the integrated academic and grey literature review.
• Identifying implementation challenges for AI governance within industries based on the study's findings.
• Informing policy recommendations for AI accountability based on the provided roadmap for future studies.

Strengths

• The review covers a defined time range from January 2023 to March 2025, providing a recent snapshot of the field.
• It integrates both formal academic and informal grey literature sources, offering a broad perspective.
• The dataset is shared under a permissive CC-BY-4.0 license, facilitating reuse and distribution.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for quantitative analysis.
• The 3.6 MB file size suggests a small-scale dataset, potentially limiting the depth of the underlying data.

Provenance

Source

figshare

Collection Method

Multivocal Literature Review (MLR) of academic and grey literature.

Time Range

January 2023 to March 2025

Freshness

Last updated 2026-05-28 18:14:12