arXiv:2601.07144v3 Announce Type: replace-cross Abstract: Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions. Focusing on Optimal Transport (OT), we introduce a novel notion of group fairness requiring that the probability of matching two individuals from any two given groups in the OT plan satisfies a predefined target. We first propose a modified Sinkhorn algorithm to compute perfectly fair transport plans efficiently. Since exact fairness can significantly degrade matching quality in practice, we then develop two relaxation strategies. T
The increasing deployment of AI in resource allocation and matching algorithms necessitates robust methods to ensure fairness, especially as regulatory scrutiny and public awareness grow.
This research provides practical methodologies for incorporating fairness constraints into optimal transport, which is critical for equitable distribution of scarce resources and positions in various real-world applications.
The ability to compute and relax perfectly fair matching algorithms moves the debate from aspirational fairness to implementable, albeit potentially costly, equitable resource allocation systems.
- · AI ethicists and researchers
- · Organizations deploying allocation algorithms
- · Policy makers focused on equity
- · Individuals benefiting from fairer resource distribution
- · Systems that prioritize pure efficiency over fairness
- · Organizations unwilling to compromise matching quality for equity
More widespread adoption of fair AI algorithms in areas like hiring, lending, and social services due to improved computational methods.
Increased legal and regulatory requirements for algorithmic fairness, pushing organizations to integrate these techniques as standard practice.
Societal shifts towards more equitable distributions of opportunities and resources, potentially reducing systemic biases currently propagated by opaque or unfair algorithms.
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