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Quota-based debiasing can decrease representation of the most under-represented groups.

I Smirnov^1,2, F Lemmerich³, M Strohmaier^1,4

¹Department for Society, Technology and Human Factors and Department of Computer Science, RWTH Aachen University, Aachen, Nordrhein-Westfalen, Germany.

Royal Society Open Science

|September 27, 2021

Summary

Introducing quotas to fix bias in selection processes can backfire. Debiasing based on one attribute may harm underrepresented groups, especially when attributes are correlated, highlighting the need for holistic solutions.

Keywords:

algorithmic fairness debiasing intersectionality quota

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Area of Science:

Social Sciences
Computer Science
Data Science

Background:

Societal decisions like admissions and hiring often involve selecting top candidates.
Selection processes can exhibit biases, leading to under-representation of certain demographic groups.
Debiasing methods, such as quotas, are commonly used to address representation issues.

Purpose of the Study:

To investigate the impact of single-attribute quota-based debiasing on group representation.
To analyze the effects of debiasing on intersectional groups when attributes are correlated.
To evaluate the overall fairness implications of common debiasing strategies.

Main Methods:

Analysis of selection processes with correlated binary attributes.
Simulation of quota-based debiasing strategies applied to a single attribute.
Measurement of group representation and fairness metrics before and after debiasing.

Main Results:

Quota-based debiasing on a single attribute can exacerbate under-representation for intersectional groups.
Fairness can decrease overall, particularly for the most disadvantaged intersectional subgroups.
The effectiveness of debiasing is contingent on considering all relevant attributes.

Conclusions:

Single-attribute debiasing is insufficient and can lead to unintended negative consequences.
Holistic debiasing approaches that consider all relevant attributes are crucial for equitable selection.
Addressing the root causes of inequality is more effective than purely numerical solutions for debiasing.