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Explainable and transparent artificial intelligence for public policymaking

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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

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- [Home](https://www.cambridge.org/core)
- > [Journals](https://www.cambridge.org/core/publications/journals)
- > [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...

![](https://static.cambridge.org/covers/DAP_0_0_0/data-and-policy.jpg)[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

... (truncated, 98 KB total)
Resource ID: 232261cfe3c236b9 | Stable ID: ODI1NTBhYz