Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
The price of unfairness in linear bandits with biased feedback
Authors: Solenne Gaucher, Alexandra Carpentier, Christophe Giraud
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the problem of fair sequential decision making with biased linear bandit feedback. At each round, a player selects an action described by a covariate and by a sensitive attribute. The perceived reward is a linear combination of the covariates of the chosen action, but the player only observes a biased evaluation of this reward, depending on the sensitive attribute. To characterize the difficulty of this problem, we design a phased elimination algorithm that corrects the unfair evaluations, and establish upper bounds on its regret. We show that the worst-case regret is smaller than O( 1/3 log(T)1/3T 2/3), where is an explicit geometrical constant characterizing the difficulty of bias estimation. We prove lower bounds on the worst-case regret for some sets of actions showing that this rate is tight up to a possible sub-logarithmic factor. We also derive gap-dependent upper bounds on the regret, and matching lower bounds for some problem instance. |
| Researcher Affiliation | Academia | Solenne Gaucher Département de Mathématiques d Orsay, Université Paris-Saclay, Orsay, France EMAIL Alexandra Carpentier Institut für Mathematik, Universität Potsdam, Potsdam, Germany EMAIL Christophe Giraud Département de Mathématiques d Orsay, Université Paris-Saclay, Orsay, France EMAIL |
| Pseudocode | Yes | Routine 1 G-EXP-ELIM (X, n, ) Routine 2 -EXP-ELIM (X, (X (z), b (z))z2{ 1,1}, b , n, ) Algorithm 3 FAIR PHASED ELIMINATION (sketched) |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets, thus no dataset availability information is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore no dataset splits for validation are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments that would require specific hardware. No hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments that would require specific software dependencies with version numbers. No such details are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |