Controlled Model Debiasing through Minimal and Interpretable Updates
Authors: Federico Di Gennaro, Thibault Laugel, Vincent Grari, Marcin Detyniecki
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments, we demonstrate that our method achieves comparable fairness and accuracy performance to existing algorithmic fairness approaches, while requiring fewer prediction changes. Additionally, we show that COMMOD enables more meaningful and easier-to-understand prediction changes, enhancing its utility in practice. To summarize, our contributions are as follows: We validate its performance through experiments on classical fairness datasets, showcasing its debiasing efficacy and ability to perform fewer and more interpretable changes (Section 6). |
| Researcher Affiliation | Collaboration | Federico Di Gennaro1,2 EMAIL EPFL, Lausanne, Switzerland Thibault Laugel1 EMAIL AXA, Paris, France TRAIL, LIP6, Sorbonne Université, Paris, France Vincent Grari AXA, Paris, France TRAIL, LIP6, Sorbonne Université, Paris, France Marcin Detyniecki AXA, Paris, France TRAIL, LIP6, Sorbonne Université, Paris, France Polish Academy of Science, IBS PAN, Warsaw, Poland |
| Pseudocode | Yes | In this section we give a more detailed walk-through of our end-to-end training procedure for COMMOD, as well as a compact pseudocode listing. Algorithm 1 COMMOD Training (simplified pseudocode) |
| Open Source Code | Yes | Code to reproduce the experiments is available on the following repository: https://github.com/axa-rev-research/controlled-model-debiasing |
| Open Datasets | Yes | We experimentally validate two binary classification datasets, commonly used in the fairness literature (Hort et al., 2024): Law School (Wightman, 1998) and Compas (Angwin et al., 2016). |
| Dataset Splits | Yes | After splitting each dataset in Dtrain (70%) and Dtest (30%), we train our pretrained classifier f to optimize solely accuracy. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions general computing environments without specific hardware. |
| Software Dependencies | No | In these experiments, we use a Logistic Regression classifier from the scikit-learn library, but any other classifier could be used since COMMOD and the proposed competitors are model-agnostic. |
| Experiment Setup | Yes | For COMMOD, we set a fixed value for the number of concepts k: 2 for Law School and 5 for Compas. Further details on implementation are available in Section C of the Appendix. ... The range of values we tested remained consistent across different datasets, with λfair 10, λratio 0.5, and λconcepts 1. |