Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
Authors: David Madras, Toni Pitassi, Richard Zemel
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that learning to defer can make systems not only more accurate but also less biased. |
| Researcher Affiliation | Academia | David Madras, Toniann Pitassi & Richard Zemel University of Toronto Vector Institute {madras,toni,zemel}@cs.toronto.edu |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm', nor are there structured code-like blocks describing procedures. |
| Open Source Code | Yes | Code available at https://github.com/dmadras/predict-responsibly. |
| Open Datasets | Yes | We use two datasets: COMPAS [26], where we predict a defendant s recidivism without discriminating by race, and Heritage Health (https://www.kaggle.com/c/hhp)... |
| Dataset Splits | No | The paper mentions 'All results are on held-out test sets' and that models are trained, but does not explicitly provide details about training/validation/test splits, specific percentages, or how a validation set was used for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU or GPU models, memory, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers, such as programming language versions or library versions (e.g., TensorFlow, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | We train all models and DMs with a fully-connected two-layer neural network... We show results across various hyperparameter settings (αfair, γdefer/γreject)... To simulate high-bias DMs (scen. 2) we train a regularized model with αfair = 0.1... To create inconsistent DMs (scen. 3), we flip a subset of the DM s predictions post-hoc with 30% probability... |