Software

  • FairCORELS: Open-source python module for learning fair rule lists.
    • PyPI project page: here
    • Source code repository: here
    • Paper: original method introduced here, and presented in a demo paper at the CIKM 2021 conference
    • A hands-on tutorial and examples: here
    • Short description: FairCORELS is a learning algorithm building rule lists (that are inherently interpretable models) under statistical/group fairness constraints. It is an extension of the CORELS algorithm, modified to also take into account fairness considerations. More precisely, given a particular notion of fairness, FairCORELS builds a rule list minimizing CORELS' original objective function and meeting the fairness requirement.
  • FairCORELSV2: New version of the FairCORELS package, specifically designed to learn optimal fair models, and embedding advanced techniques to efficiently explore the search space of fair rule lists.
    • PyPI project page: here
    • Source code repository: here
    • Paper: here (version accepted at the CPAIOR 2022 conference)
    • Short description: FairCORELSV2 embedds a novel pruning method, leveraging Mixed Integer Linear Programming to prune the search space of fair rule lists efficiently, considering jointly the objective function and the fairness constraint. It also provides a new prefix permutation map that can be used to speed up the learning process while preserving the guarantee of optimality.