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<p>Prior work suggests automated dispute resolution tools using “provably fair” algorithms can address disparities between demographic groups. These methods use multi-criteria elicited preferences from all disputants and satisfy constraints to generate “fair” solutions. However, we analyze the potential for inequity to permeate proposals through the preference elicitation stage. This possibility arises if differences in dispositional attitudes differ between demographics, and those dispositions affect elicited preferences. Specifically, risk aversion plays a prominent role in predicting preferences. Risk aversion predicts a weaker relative preference for <i>salary</i> and a softer within-issue utility for each issue; this leads to worse compensation packages for risk-averse groups. These results raise important questions in AI-value alignment about whether an AI mediator should take explicit preferences at face value. </p>

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“Provably fair” algorithms may perpetuate racial and gender bias: a study of salary dispute resolution

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月12日

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  • James Hale,
  • Peter H. Kim,
  • Jonathan Gratch

摘要

Prior work suggests automated dispute resolution tools using “provably fair” algorithms can address disparities between demographic groups. These methods use multi-criteria elicited preferences from all disputants and satisfy constraints to generate “fair” solutions. However, we analyze the potential for inequity to permeate proposals through the preference elicitation stage. This possibility arises if differences in dispositional attitudes differ between demographics, and those dispositions affect elicited preferences. Specifically, risk aversion plays a prominent role in predicting preferences. Risk aversion predicts a weaker relative preference for salary and a softer within-issue utility for each issue; this leads to worse compensation packages for risk-averse groups. These results raise important questions in AI-value alignment about whether an AI mediator should take explicit preferences at face value.