Too Relaxed to Be Fair
Authors: Michael Lohaus, Michael Perrot, Ulrike Von Luxburg
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate Search Fair by comparing it to 5 baselines on 6 real-world datasets. |
| Researcher Affiliation | Academia | 1 University of Tubingen, Germany 2 Max Planck Institute for Intelligent Systems, Tubingen, Germany 3 Univ Lyon, UJM-Saintsimilarity-based classifiers to exhibit sufficient conditions Etienne, CNRS, IOGS, Lab HC UMR 5516, F-42023, SAINTthat guarantee the existence of a fair and accurate classifier. ETIENNE, France |
| Pseudocode | Yes | We call this procedure Search Fair and summarize it in Algorithm 1 in the supplementary. |
| Open Source Code | Yes | The code is freely available online: github.com/mlohaus/Search Fair. |
| Open Datasets | Yes | We consider different datasets: Celeb A (Liu et al., 2015), Adult (Kohavi & Becker, 1996), Dutch (Zliobaite et al., 2011), COMPAS (Larson et al., 2016), Communities and Crime (Redmond & Baveja, 2002), and German Credit (Dua & Graff, 2017). |
| Dataset Splits | Yes | We use 5-fold cross validation to choose other hyperparameters. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, TensorFlow 2.x). |
| Experiment Setup | Yes | We use 5-fold cross validation to choose other hyperparameters. [...] For all remaining methods we need to choose the regularization parameter β and the width of the rbf kernel. These values are respectively chosen in the sets 10−6, 10−5, 10−4, 10−3, 10−2 and {10log d−1 , 10log d , d, 10log d+1 , 10log d+2 }, with d the number of features. |