Noise-tolerant fair classification
Authors: Alex Lamy, Ziyuan Zhong, Aditya K. Menon, Nakul Verma
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 a.lamy@columbia.edu Ziyuan Zhong Columbia University ziyuan.zhong@columbia.edu Aditya Krishna Menon Google adityakmenon@google.com Nakul Verma Columbia University verma@cs.columbia.edu |
| 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. |