Fair Classification with Counterfactual Learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Recent advances in machine learning have led to emerging new approaches to deal with different kinds of biases that exist in the data. On the one hand, counterfactual learning copes with biases in the policy used for sampling (or logging) the data in order to evaluate and learn new policies. On the other hand, fairness-aware learning aims at learning fair models to avoid discrimination against certain individuals or groups. In this paper, we design a counterfactual framework to model fairness-aware learning which benefits from counterfactual reasoning to achieve more fair decision support systems. We utilize a definition of fairness to determine the bandit feedback in the counterfactual setting that learns a classification strategy from the offline data, and balances classification performance versus fairness measure. In the experiments, we demonstrate that a counterfactual setting can be perfectly exerted to learn fair models with competitive results compared to a well-known baseline system.

Original languageEnglish
Title of host publicationSIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
Number of pages4
ISBN (Electronic)9781450380164
Publication statusPublished - 25 Jul 2020

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  • classification
  • counterfactual reasoning
  • fairness-aware learning


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