Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Too Relaxed to Be Fair
Authors: Michael Lohaus, Michael Perrot, Ulrike Von Luxburg
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,461 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,578 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |