Huaxiu Yao, James Zou, Kenji Kawaguchi, Zhun Deng, Ting Ye, Yuzhen Mao
We lifted 14 functions out of this paper's own repositories and ran 13 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| modar7/the_fairness_stitch | pwc_unofficial | 13 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy_equality_difference | Ran | modar7/the_fairness_stitch/src/metrics.py code served (permissive licence) · get_code("b113af2bdb4b7ad0") |
| ae_constraint | Ran | modar7/the_fairness_stitch/src/fairness_constraints.py code served (permissive licence) · get_code("3e2bcc37f0d29b39") |
| compute_auc | Ran | modar7/the_fairness_stitch/src/utils_abroca.py code served (permissive licence) · get_code("f34aff34be2461e9") |
| compute_roc | Ran | modar7/the_fairness_stitch/src/utils_abroca.py code served (permissive licence) · get_code("5bf52700b6ec83bf") |
| eo_constraint | Ran | modar7/the_fairness_stitch/src/fairness_constraints.py code served (permissive licence) · get_code("d1f364aa232405ad") |
| get_pred | Ran | modar7/the_fairness_stitch/src/utils.py code served (permissive licence) · get_code("c691b280c17d59c0") |
| get_pred_Stitched_Model | Ran | modar7/the_fairness_stitch/src/utils.py code served (permissive licence) · get_code("0f0267a4b0a6ffd7") |
| interpolate_roc_fun | Ran | modar7/the_fairness_stitch/src/utils_abroca.py code served (permissive licence) · get_code("833b23ea24be2333") |
| max_min_fairness | Ran | modar7/the_fairness_stitch/src/metrics.py code served (permissive licence) · get_code("33a0eb0cb76eac7a") |
| mmf_constraint | Ran | modar7/the_fairness_stitch/src/fairness_constraints.py code served (permissive licence) · get_code("1fda7cb005b7308f") |
| prepare_data | Ran | modar7/the_fairness_stitch/src/data_processing.py code served (permissive licence) · get_code("c700a303a2482780") |
| print_acc_auc_stats | Ran | modar7/the_fairness_stitch/src/utils.py code served (permissive licence) · get_code("3016678b713de42a") |
| valid_per_epoch | Ran | modar7/the_fairness_stitch/src/FDR.py code served (permissive licence) · get_code("69501a60076aa16a") |
| compute_abroca | Not yet run | modar7/the_fairness_stitch/src/compute_abroca.py code served (permissive licence) · get_code("77748e5ebfbb1e32") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
As machine learning has been deployed ubiquitously across applications in modern data science, algorithmic fairness has become a great concern. Among them, imposing fairness constraints during learning, i.e. in-processing fair training, has been a popular type of training method because they don't require accessing sensitive attributes during test time in contrast to post-processing methods. While this has been extensively studied in classical machine learning models, their impact on deep neural networks remains unclear. Recent research has shown that adding fairness constraints to the objective function leads to severe over-fitting to fairness criteria in large models, and how to solve this challenge is an important open question. To tackle this, we leverage the wisdom and power of pre-training and fine-tuning and develop a simple but novel framework to train fair neural networks in an efficient and inexpensive way -lastlayer fine-tuning alone can effectively promote fairness in deep neural networks. This framework offers valuable insights into representation learning for training fair neural networks. The code is published at https://github.com/ yuzhenmao/Fairness-Finetuning
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2304.03935")
get_code_for_paper("2304.03935")
have("2304.03935")
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