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It's not a bug, it's a feature: How AI experts and data scientists account for the opacity of algorithms
1Department of Sociology, Western University, London, ON, Canada.
Social Studies of Science
|September 15, 2025
Summary
Machine learning (ML) experts view algorithm opacity not as a flaw, but as a virtue, embracing unexpected outcomes. This perspective emerged from ML
Area of Science:
- Sociology of algorithms
- Science and Technology Studies (STS)
- Artificial Intelligence (AI) ethics
Background:
- Opacity of machine learning (ML) algorithms is a significant concern.
- Sociology of algorithms posits opacity is socially constructed, not inherent.
- Previous focus on mechanical objectivity contrasted human inconsistency with machine reliability.
Purpose of the Study:
- To investigate the epistemic culture of ML experts regarding algorithm opacity.
- To demonstrate how ML experts valorize opacity as an epistemic virtue.
- To trace the social and historical origins of this valorization.
Main Methods:
- Sixty in-depth, semi-structured interviews with ML experts and data scientists.
- Historical research on the origins of data science and ML.
- Qualitative analysis of expert interviews and historical documents.
Main Results:
- ML experts embrace algorithm opacity, viewing unexpected outcomes as a sign of advanced predictive capacity.
- Opacity is transformed from a problem into an epistemic virtue within ML.
- This valorization is linked to jurisdictional struggles differentiating ML from AI expert systems and inferential statistics.
Conclusions:
- The epistemic culture of ML experts actively constructs opacity as a virtue.
- This construction aids in establishing ML expertise and differentiating it from related fields.
- Understanding this social construction is crucial for addressing concerns about ML transparency and accountability.
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