Explainable and transparent artificial intelligence for public policymaking

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Cambridge University Press(peer-reviewed)·cambridge.org/core/journals/data-and-policy/article/expla...

Credibility Rating

5/5

Gold(5)

Gold standard. Rigorous peer review, high editorial standards, and strong institutional reputation.

Rating inherited from publication venue: Cambridge University Press

Relevant to AI governance practitioners and policymakers seeking frameworks for deploying interpretable AI in government settings; offers a practical architecture rather than purely theoretical guidance on XAI for public sector use.

Metadata

Importance: 52/100journal articleprimary source

Summary

This paper proposes a reference architecture and virtualized policy management environment for deploying explainable machine learning models in public policymaking contexts. It examines the transition from black-box AI models to interpretable alternatives, validating the approach through real policymaking case studies. The work addresses how governments can responsibly adopt AI-driven decision support while maintaining transparency and accountability.

Key Points

•Proposes a reference architecture (RA) for integrating explainable ML models into public policy management environments
•Examines the spectrum from opaque black-box models to interpretable/explainable AI for government decision support
•Validates the framework through real-world policymaking cases, bridging theory and practical governance application
•Addresses accountability and transparency requirements essential for democratic legitimacy of AI-assisted policy decisions
•Published in Cambridge's Data & Policy journal, signaling relevance to data governance and evidence-based policymaking communities

Cited by 1 page

Page	Type	Quality
AI-Driven Institutional Decision Capture	Risk	73.0

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Resource ID: 232261cfe3c236b9 | Stable ID: sid_t3IUoenBpV