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This paper is primarily about machine learning: a training method for gradient-boosted decision trees that certifies algorithmic recourse, meaning people denied a decision get an affordable path to reversing it. It is clearly in scope, so it is categorized under Machine Learning. The results are reported against a single prior baseline (RABIT) under that baseline's own protocol, so the gains should be read as promising but not yet independently validated across broader settings. For ML practitioners working on fairness and credit or lending models, the practical takeaway is that recourse can be guaranteed at a chosen rate rather than only approximated, and the certifier is claimed to work on existing XGBoost and LightGBM ensembles.
CERBO: Certified Recourse at a Requested Rate for Gradient-Boosted Trees
arXiv:2611.03222v1 Announce Type: cross Abstract: Recourse-aware learning trains models so that people who receive an adverse decision can reverse it with an affordable change. For gradient-boosted decision trees (GBDTs), the existing method, RABIT, exposes a single recourse weight whose effect on the recourse rate is abrupt and data-dependent, and its training bound cannot certify recourse for any rejected individual. We present Certified Recourse Boosting (CERBO), which takes a requested recourse rate and returns a GBDT with certified actions: it tracks exact additive effort along tree paths, trains against committed boxes (a family of bounds that contains RABIT's and certifies a within-budget action for each training sample) and adjusts the weight after every tree until the certified rate meets the request. A calibration step guarantees the rate on new individuals, and the certifier runs on any tree ensemble, including XGBoost and LightGBM, in milliseconds per person without a solver. In RABIT's own experimental protocol, at equal accuracy, CERBO raises the recourse ratio from 0.90 to 0.99 on FICO and from 0.64 to 0.77 on Bail. Under additive costs on six data sets, it raises the area under the accuracy-recourse frontier in all 12 settings, and in simulated deployments it guarantees recourse to 70% of rejected individuals in 11 of 12 settings, against 2 for RABIT.
Abstract:Recourse-aware learning trains models so that people who receive an adverse decision can reverse it with an affordable change. For gradient-boosted decision trees (GBDTs), the existing method, RABIT, exposes a single recourse weight whose effect on the recourse rate is abrupt and data-dependent, and its training bound cannot certify recourse for any rejected individual. We present Certified Recourse Boosting (CERBO), which takes a requested recourse rate and returns a GBDT with certified actions: it tracks exact additive effort along tree paths, trains against committed boxes (a family of bounds that contains RABIT's and certifies a within-budget action for each training sample) and adjusts the weight after every tree until the certified rate meets the request. A calibration step guarantees the rate on new individuals, and the certifier runs on any tree ensemble, including XGBoost and LightGBM, in milliseconds per person without a solver. In RABIT's own experimental protocol, at equal accuracy, CERBO raises the recourse ratio from 0.90 to 0.99 on FICO and from 0.64 to 0.77 on Bail. Under additive costs on six data sets, it raises the area under the accuracy-recourse frontier in all 12 settings, and in simulated deployments it guarantees recourse to 70% of rejected individuals in 11 of 12 settings, against 2 for RABIT.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
arXiv:2611.03222 [cs.LG]
(or arXiv:2611.03222v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.03222 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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