[Re] Fairness Guarantees under Demographic ShiftDownload PDF

Published: 02 Aug 2023, Last Modified: 02 Aug 2023MLRC 2022Readers: Everyone
Keywords: Fairness and Bias in ML, Fair Machine Learning, Reproduction, rescience c, machine learning
TL;DR: Reproducing Shifty, a family of algorithms which guarantees fairness even under demographic shift in the deployment distribution.
Abstract: Scope of Reproducibility: The original authors' main contribution is the family of Shifty algorithms, which can guarantee that certain fairness constraints will hold with high confidence even after a demographic shift in the deployment population occurs. They claim that Shifty provides these high-confidence fairness guarantees without a loss in model performance, given enough training data. Methodology: The code provided by the original paper was used, and only some small adjustments needed to be made in order to reproduce the experiments. All model specifications and hyperparameters from the original implementation were used. Extending beyond reproducing the original paper, we investigated the sensibility of Shifty to the size of the bounding intervals limiting the possible demographic shift, and ran shifty with an additional optimization method. Results: Our results approached the results reported in the original paper. They supported the claim that \textit{Shifty} reliably guarantees fairness under demographic shift, but could not verify that Shifty performs at no loss of accuracy. What was easy: The theoretical framework laid out in the original paper was well explained and supported by additional formulas and proofs in the appendix. Further, the authors provided clear instructions on how to run the experiments and provided necessary hyperparameters. What was difficult: While an open-source implementation of Shifty was provided and was debugged with relatively low time investment, the code did not contain extensive documentation and was complex to understand. It was therefore difficult to verify that each part of the code functions as expected and to expand upon the existing experiments. Further, certain hyperparameter and model specifications deviated between the provided code and the original paper, which made it challenging to know which specifications to apply when reproducing. Communication with original authors: The first author of the original paper was contacted, but we have yet to receive a reply.
Paper Url: https://iclr.cc/virtual/2022/poster/6666
Paper Review Url: https://openreview.net/forum?id=wbPObLm6ueA
Paper Venue: ICLR 2022
Confirmation: The report pdf is generated from the provided camera ready Google Colab script, The report metadata is verified from the camera ready Google Colab script, The report contains correct author information., The report contains link to code and SWH metadata., The report follows the ReScience latex style guides as in the Reproducibility Report Template (https://paperswithcode.com/rc2022/registration)., The report contains the Reproducibility Summary in the first page., The latex .zip file is verified from the camera ready Google Colab script
Latex: zip
Journal: ReScience Volume 9 Issue 2 Article 13
Doi: https://www.doi.org/10.5281/zenodo.8173680
Code: https://archive.softwareheritage.org/swh:1:dir:436c48ce9cf36b10ee3cdcd537a06c9df2cd53cc
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