Testing Group Fairness via Optimal Transport Projections
Authors: Nian Si, Karthyek Murthy, Jose Blanchet, Viet Anh Nguyen
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Numerical Experiments Our experiments use the following three datasets: Arrhythmia (Dua & Graff, 2017), COMPAS (Multi Media LLC, 2016) and Drug (Fehrman et al., 2017). The details of the datasets are provided in Appendix B.2. In the first experiment, we test the fairness of the Tikhonovregularized logistic and SVM classifiers by the equal opportunity criterion. We randomly split 70%-30% of the data as a train-test set. Figure 1 reports the test statistics, fairness rejection threshold, and the accuracy of the classifier. |
| Researcher Affiliation | Collaboration | 1Department of Management Science & Engineering, Stanford University 2Engineering Systems and Design pillar, Singapore University of Technology and Design 3Vin AI Research, Vietnam. |
| Pseudocode | Yes | Algorithm 1 Computing P(ˆPN) for one-dimensional φ( ) 1: Input: Data {d(xi), C(xi), φ(ui, EˆPN [U])}N i=1. 2: Output: the optimal value P(ˆPN). 3: Let s P i [N] C(xi)φ(ui, EˆPN [U]); 4: For i [N], compute ti d(xi) 1(1 2C(xi))φ(ui, EˆPN [U])sgn(s); |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | Our experiments use the following three datasets: Arrhythmia (Dua & Graff, 2017), COMPAS (Multi Media LLC, 2016) and Drug (Fehrman et al., 2017). The details of the datasets are provided in Appendix B.2. |
| Dataset Splits | No | We randomly split 70%-30% of the data as a train-test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions 'Tikhonov-regularized logistic and SVM classifiers' and plots results against a 'regularization parameter λ', but it does not specify concrete experimental setup details such as learning rates, batch sizes, optimizers, or other hyperparameters needed for replication. |