aiqbench analysis
This research investigates the economic incentives for decision-makers like banks and employers to provide algorithmic recourse, revealing that optimal policies often involve offering this option only to applicants with intermediate scores rather than universally. The study demonstrates a trade-off where providing recourse helps marginal candidates gain acceptance while simultaneously imposing costly hurdles on high-scoring applicants who would otherwise be accepted outright. This analysis advances the understanding of how game-theoretic models shape the implementation of fairness mechanisms in automated screening systems.
Incentives to Offer Algorithmic Recourse
arXiv:2301.12884v2 Announce Type: replace-cross Abstract: Algorithmic recourse promises to help applicants rejected by automated systems by explaining the changes needed to secure acceptance. What incentive do decision-makers, such as banks and employers, have to offer recourse? We study this question in a screening model in which recourse is both productive and selective: completing recourse improves an applicant's value to the decision-maker, but applicants differ in their cost of completion. The optimal policy is a threshold rule: reject applicants with low scores, offer recourse to an intermediate range of scores, and accept applicants with high scores outright. Because the intermediate range spans the cutoff that would separate acceptance from rejection when recourse is not available, some marginal applicants gain a new path to acceptance, while others---who would have been accepted outright---must now clear a costly hurdle.
Abstract:Algorithmic recourse promises to help applicants rejected by automated systems by explaining the changes needed to secure acceptance. What incentive do decision-makers, such as banks and employers, have to offer recourse? We study this question in a screening model in which recourse is both productive and selective: completing recourse improves an applicant's value to the decision-maker, but applicants differ in their cost of completion. The optimal policy is a threshold rule: reject applicants with low scores, offer recourse to an intermediate range of scores, and accept applicants with high scores outright. Because the intermediate range spans the cutoff that would separate acceptance from rejection when recourse is not available, some marginal applicants gain a new path to acceptance, while others---who would have been accepted outright---must now clear a costly hurdle.
Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI)
arXiv:2301.12884 [cs.GT]
(or arXiv:2301.12884v2 [cs.GT] for this version)
https://doi.org/10.48550/arXiv.2301.12884 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 228 words
