aiqbench analysis
This research addresses the critical challenge of integrating unreliable AI predictions into online allocation systems without compromising efficiency or fairness. By combining conservative fallback mechanisms with explicit fairness corrections, the study demonstrates how to maintain robustness against adversarial advice and mitigate exposure disparities. The work provides theoretical guarantees for loss proportional to prediction error, offering a practical framework for deploying learning-augmented algorithms in high-stakes environments.
Learning-Augmented Online Allocation under Unreliable Advice: Robustness, Exposure Fairness, and Distribution Shift
arXiv:2608.26889v1 Announce Type: new Abstract: Learning-augmented algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and fairness. We study an online allocation problem with finite candidate sets, irreversible decisions, and exposure constraints. We propose a robust and fair rule combining advice with a conservative fallback and fairness correction. Under bounded-error assumptions, we prove consistency and robustness with loss proportional to prediction error. Experiments show stability under adversarial advice and significant reductions in exposure disparity.
Abstract:Learning-augmented algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and fairness. We study an online allocation problem with finite candidate sets, irreversible decisions, and exposure constraints. We propose a robust and fair rule combining advice with a conservative fallback and fairness correction. Under bounded-error assumptions, we prove consistency and robustness with loss proportional to prediction error. Experiments show stability under adversarial advice and significant reductions in exposure disparity.
Artificial Intelligence (cs.AI)
arXiv:2608.26889 [cs.AI]
(or arXiv:2608.26889v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.26889 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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