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..
Noise-tolerant fair classification
Authors: Alex Lamy, Ziyuan Zhong, Aditya K. Menon, Nakul Verma
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We finally show that our procedure is empirically effective on two case-studies involving sensitive feature censoring. and We demonstrate that it is viable to learn fair classifiers given noisy sensitive features. |
| Researcher Affiliation | Collaboration | Alexandre Lamy Columbia University EMAIL Ziyuan Zhong Columbia University EMAIL Aditya Krishna Menon Google EMAIL Nakul Verma Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1 Reduction-based algorithm for fair classification given noisy A. Input: Training set S = {(xi, yi, ai)}n i=1, scorer class F, fairness tolerance 0, fairness constraint ( ), fair classification algorithm Fair Alg, noise estimation algorithm Noise Est Output: Fair classifier f 2 F 1: ˆ , ˆβ Noise Est(S) 2: 0 (1 ˆ ˆβ) 3: return Fair Alg(S, F, , 0) |
| Open Source Code | Yes | Source code is available at https://github.com/AIasd/noise_fairlearn. |
| Open Datasets | Yes | We look at COMPAS, a dataset from Propublica (Angwin et al., 2016) and We consider the dataset law school, which is a subset of the original dataset from LSAC (Wightman, 1998). |
| Dataset Splits | No | The paper specifies a random 80-20 training-testing split for its experiments (e.g., Section 5.3, 5.4) but does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not specify the versions of any key software components or libraries used for the implementation or experiments. |
| Experiment Setup | No | While the paper details the noise settings for the datasets (e.g., CCN noise with + = = 0.15 is added to the sensitive attribute), it does not explicitly provide specific hyperparameters or system-level training settings for the classification model, such as learning rates, batch sizes, or optimizer configurations. |