What question did this study set out to answer?

The aim is to explore how traditional computer science concepts affect fairness in machine learning within social contexts.

January 9, 2019Open Access

Fairness and Abstraction in Sociotechnical Systems

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The paper reveals challenges in achieving fairness in machine learning systems impacting societal outcomes.

The aim is to explore how traditional computer science concepts affect fairness in machine learning within social contexts.
Identified five 'traps' in fair-ML work that hinder context-awareness.
Analyzed case studies from Science and Technology Studies to explain these traps.
Proposed strategies for technical designers to incorporate social actors into the design process.
Highlighted the ineffectiveness and inaccuracies of technical interventions in societal contexts.
Illustrated how a focus on process over solutions can mitigate these pitfalls.
Provided recommendations for drawing abstraction boundaries that include social considerations.

Selbst et al. (2019) studied this question.

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