Group-Aware Threshold Adaptation for Fair Classification
Authors: Taeuk Jang, Pengyi Shi, Xiaoqian Wang6988-6995
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our method outperforms state-of-the-art methods and obtains the result that is closest to the theoretical accuracy-fairness tradeoff boundary. |
| Researcher Affiliation | Academia | 1School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA, 47907 2Krannert School of Management, Purdue University, West Lafayette, USA, 47907 |
| Pseudocode | No | The paper does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | We evaluate the methods on four fairness datasets: Celeb A dataset (Liu et al. 2015), Adult dataset (Kohavi 1996), COMPAS1 dataset, and German dataset (Dua and Graff 2019). 1https://github.com/propublica/compas-analysis |
| Dataset Splits | No | The paper mentions datasets but does not explicitly provide training/validation/test dataset splits. It states: 'More details of the comparing methods, evaluation metrics, and datasets are provided in the Supplementary.' |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models or types) 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) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or optimizer settings. |