Fairness without Demographics through Adversarially Reweighted Learning
Authors: Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results show that ARL improves Rawlsian Max-Min fairness, with notable AUC improvements for worst-case protected groups in multiple datasets, outperforming state-of-the-art alternatives. ... In Section 4, we evaluate ARL on three real-world datasets. Our results show that ARL yields significant AUC improvements for worst-case protected groups, outperforming state-of-the-art alternatives on all the datasets, and even improves the overall AUC on two of three datasets. |
| Researcher Affiliation | Collaboration | Preethi Lahoti plahoti@mpi-inf.mpg.de Max Planck Institute for Informatics Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi Google Research |
| Pseudocode | No | The paper includes a computational graph (Figure 2) but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not contain any statements or links indicating that open-source code for the methodology is provided. |
| Open Datasets | Yes | We now demonstrate the effectiveness of our proposed ARL approach through experiments over three real datasets3 well used in the fairness literature: (i) Adult [45]: income prediction (ii) LSAC [52]: law school admission and (iii) COMPAS [1]: recidivism prediction. ... [45] B. Becker R. Kohavi. 1996. UCI ML Repository. http://archive.ics.uci.edu/ml |
| Dataset Splits | Yes | Best hyper-parameter values for all approaches are chosen via grid-search by performing 5-fold cross validation optimizing for best overall AUC. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., GPU/CPU models, memory specifications, or cloud resources). |
| Software Dependencies | No | The paper describes architectural details such as 'feed-forward network' and 'Re LU activation function', but does not list any specific software libraries with version numbers (e.g., Python, TensorFlow, PyTorch). |
| Experiment Setup | Yes | Our model for the learner is a fully connected two layer feed-forward network with 64 and 32 hidden units in the hidden layers, with Re LU activation function. While our adversary is general enough to be a deep network, we observed that for the small academic datasets used in our experiments, a linear adversary performed the best. ... Best hyper-parameter values for all approaches are chosen via grid-search by performing 5-fold cross validation optimizing for best overall AUC. |