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 [1].
Empirical Likelihood for Fair Classification
Authors: Pangpang Liu, Yichuan Zhao
ICLR 2024 | Venue PDF | 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 EMAIL Yichuan Zhao Department of Mathematics and Statistics Georgia State University Atlanta, GA 30303, USA EMAIL |
| 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). |