Explaining machine learning reveals policy challenges
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The need to make objectives explicit may expose policy trade-offs that had previously been implicit and obscured
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Policymaking typically involves trade-offs between competing social values, whereas ML algorithms require explicitly defined objectives and weighing competing objectives.
“For instance, policymaking typically involves trade-offs between competing social values (Parkhurst, 2016; Saltelli and Giampietro, 2017), whereas ML algorithms require explicitly defined objectives and weighing competing objectives (Coyle and Weller, 2020).”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Coyle and Weller, 2020
Policy decision-making is a complex, human, and political process, and there are many challenges to using ML in this domain.
“However, it is important to note that policy decision-making is a complex, human, and political process, and there are many challenges to using ML in this domain.”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Coyle and Weller, 2020
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Explainable machine learning for public policy: Use cases, gaps, and research directions
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- Authors
- Coyle, D., & Weller, A.
- Affiliations
- University of Cambridge (GB)
- Venue
- Science
- Published
- 2020-06-26
- Volume
- 368
- Issue
- 6498
- Pages
- 1433-1434
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3037321588
- Cited by
- 74
- References
- 17
- Citations by year
- 2026: 4 · 2025: 18 · 2024: 12 · 2023: 15 · 2022: 13 · 2021: 11 · 2020: 1
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Apollo
- Funders
- Alan Turing Institute (TU/B/000074)
- Leverhulme Trust (RC-2015-067)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- explainable machine learning, algorithmic transparency, policy trade-offs, algorithmic governance, automated decision-making
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