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Cited by 25Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIPhilosophy of Biology and Scientific Explanation

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Beyond model interpretability: socio-structural explanations in machine learning

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Smart, Andrew, Kasirzadeh, Atoosa·2025·AI & SOCIETY
DOI 10.1007/s00146-024-02056-1
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  • The usefulness or correctness of model interpretations can depend on the broader context of the model's development or deployment.

    “Another potential reason for lack of clarity is that models are often studied in a vacuum: Smart & Kasirzadeh (2024) emphasize that the usefulness or correctness of model interpretations can depend on the broader context of the model's development or deployment.”

    From Open Problems in Mechanistic Interpretability (2025) · cites this paper as Smart & Kasirzadeh, 2024

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Metadata

Authors
Smart, A., & Kasirzadeh, A.
Affiliations
  • Google (United States) (US)
  • University of Edinburgh (GB)
Corresponding author
Smart, Andrew
ORCID
Smart, Andrew
Venue
AI & SOCIETY
Publisher
Springer Science and Business Media LLC
Published
2024-09-05
Volume
40
Issue
4
Pages
2045-2053
ISSN
  • 0951-5666 (ISSN-L)
  • 1435-5655
DOI
10.1007/s00146-024-02056-1
OpenAlex
W4402275636
Cited by
25
References
8
Citations by year
2026: 13 · 2025: 10 · 2024: 2
FWCI
2.70
Type
journal article
Language
en
Open access
Licence
Locations
  • AI & Society (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
Keywords
model interpretability, explainable AI, social philosophy, social structure, algorithmic transparency, algorithmic bias, racial bias, normative domains

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