Standardized Interpretable Fairness Measures for Continuous Risk Scores
Authors: Ann-Kristin Becker, Oana Dumitrasc, Klaus Broelemann
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 contains results of experiments using benchmark data and Section 6 includes final discussion and outlook. |
| Researcher Affiliation | Industry | 1SCHUFA Holding AG, Wiesbaden, Germany. Correspondence to: Ann-Kristin Becker <ann-kristin.becker@schufa.de>, Oana Dumitrasc <oana.dumitrasc@schufa.de>, Klaus Broelemann <klaus.broelemann@schufa.de>. |
| Pseudocode | No | The paper contains mathematical definitions, theorems, and proofs but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code used for the experiments in this study is online available 5. The repository includes detailed instructions for reproducing the results. 5https://github.com/schufa-innovationlab/fair-scoring |
| Open Datasets | Yes | We use the COMPAS dataset2, the Adult dataset3 and the German Credit dataset4 to demonstrate the application of the fairness measures for continuous risk scores. 2https://raw.githubusercontent.com/propublica/compas-analysis/master/compas-scores-two-years.csv 3https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data 4https://www.kaggle.com/datasets/uciml/germancredit?resource=download |
| Dataset Splits | No | The paper states: 'Both models have been trained on 70% of the dataset and evaluated on the remaining samples.' and 'All three models have been trained on 70% of the dataset and evaluated on the remaining samples.' This describes a train/test split but does not explicitly mention a separate validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models, or cloud computing instance specifications. |
| Software Dependencies | No | The paper mentions software components like 'scipy.wasserstein_distance', 'sklearn.calibration.calibration_curve', and 'sklearn.metrics.roc_curve' in Appendix C.1, but it does not specify their version numbers. |
| Experiment Setup | No | The paper describes general aspects of the experiment setup, such as training logistic regression and XGBoost models and data preprocessing (min-max-scaling, one-hot-encoding), but it does not specify concrete hyperparameters like learning rates, batch sizes, or optimizer settings for these models. |