Explainable and transparent artificial intelligence for public policymaking
webCredibility Rating
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
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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- [Home](https://www.cambridge.org/core)
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- > [Data & Policy](https://www.cambridge.org/core/journals/data-and-policy)
- > [Volume 6](https://www.cambridge.org/core/journals/data-and-policy/volume/568D44898D353A111834DB015F9EA9A3)
- >Explainable and transparent artificial intelligence...
[Data & Policy](https://www.cambridge.org/core/journals/data-and-policy)
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## Article contents
- [Abstract](https://www.cambridge.org/core/journals/data-and-policy/article/explainable-and-transparent-artificial-intelligence-for-public-policymaking/51D4C6E27CFDEB3CD19EC5E1A6F4FAE7#sec0)
- [Policy Significance Statement](https://www.cambridge.org/core/journals/data-and-policy/article/explainable-and-transparent-artificial-intelligence-for-public-policymaking/51D4C6E27CFDEB3CD19EC5E1A6F4FAE7#sec1)
- [Introduction](https://www.cambridge.org/core/journals/data-and-policy/article/explainable-and-transparent-artificial-intelligence-for-public-policymaking/51D4C6E27CFDEB3CD19EC5E1A6F4FAE7#sec2)
- [RA and virtualized policy management environment for ML-based policies](https://www.cambridge.org/core/journals/data-and-policy/article/explainable-and-transparent-artificial-intelligence-for-public-policymaking/51D4C6E27CFDEB3CD19EC5E1A6F4FAE7#sec7)
- [ML for public policymaking: From black box to explainable models](https://www.cambridge.org/core/journals/data-and-policy/article/explainable-and-transparent-artificial-intelligence-for-public-policymaking/51D4C6E2
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