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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Group-Aware Threshold Adaptation for Fair Classification
Authors: Taeuk Jang, Pengyi Shi, Xiaoqian Wang6988-6995
AAAI 2022 | Venue PDF | 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. |