Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Too Relaxed to Be Fair
Authors: Michael Lohaus, Michael Perrot, Ulrike Von Luxburg
ICML 2020 | Venue PDF | 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. |