arXiv:2603.04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention. We study the problem of finding a fair (linear) scoring function with multiple protected groups while also minimizing the disparity from a reference scoring function. This generalizes the prior setup, which was restricted to the single-group setting without disparity minimization. Previous studies imply that the number of protec
The increasing deployment of AI systems in decision-making necessitates robust frameworks for fairness, especially as these systems are applied to complex, multi-group selection problems.
This research provides a more sophisticated approach to ensuring equitable outcomes in AI-driven selection processes, addressing a critical ethical and regulatory challenge in AI deployment.
The ability to integrate multiple protected groups and minimize disparity from reference functions expands the practical applicability and fairness of AI-based top-k selection beyond previous limitations.
- · Organizations implementing fair AI systems
- · Underrepresented groups benefiting from fairer selection
- · AI ethics researchers and developers
- · Systems with implicit biases
- · Organizations ignoring fairness in AI deployment
Improved fairness and reduced bias in AI-driven candidate selection across various domains like hiring, admissions, or resource allocation.
Increased trust and adoption of AI systems in sensitive decision-making contexts as concerns about algorithmic fairness are proactively addressed.
Potential for new regulatory standards and industry best practices to emerge, building on advanced techniques for multi-group fairness and disparity minimization.
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Read at arXiv cs.LG