Empirical Likelihood for Fair Classification
Authors: Pangpang Liu, Yichuan Zhao
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation studies show that our method exactly covers the target Type I error rate and effectively balances the trade-off between accuracy and fairness. Finally, we conduct data analysis to demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | Pangpang Liu Mitchell E. Daniels, Jr. School of Business Purdue University West Lafayette, IN 47907, USA liu3364@purdue.edu Yichuan Zhao Department of Mathematics and Statistics Georgia State University Atlanta, GA 30303, USA yichuan@gsu.edu |
| Pseudocode | No | The paper does not include a dedicated pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or include links to a code repository. |
| Open Datasets | Yes | Firstly, we apply our method on the ACS PUMS datasets (Ding et al., 2021), which encompass distribution shifts... Specifically, we use the German credit dataset (Dua & Graff, 2019), which contains 1000 instances of bank account holders and is commonly used for risk assessment prediction. |
| Dataset Splits | No | The paper mentions 'We partition the data into a training set (70%) and a test set (30%)' and 'we split the dataset into equal training and test sets.' but does not explicitly state a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We generate 2000 binary class labels uniformly at random and assign a 2-dimensional feature vector to each label by drawing samples from two distinct Gaussian distributions: p(x|y = 1) = N([2; 2], [5, 1; 1, 5]) and p(x|y = 1) = N([ 2; 2], [10, 1; 1, 3]). We use x = [cos(ϕ), sin(ϕ); sin(ϕ), cos(ϕ)]x as a rotation of the feature vector x, and draw the one-dimensional sensitive attribute s from a Bernoulli distribution, p(s = 1) = p(x |y = 1)/[p(x |y = 1) + p(x |y = 1)]. The value of ϕ controls the correlation between the sensitive attribute and the class labels. We choose ϕ = π/3, α = 0.05. We partition the data into a training set (70%) and a test set (30%) and fit a logistic model (Appendix B). |