Fairness without Harm: Decoupled Classifiers with Preference Guarantees
Authors: Berk Ustun, Yang Liu, David Parkes
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of the procedure on real-world datasets, showing that it improves accuracy without violating preference guarantees on test data. We present experiments on real-world datasets that show that our procedure can output classifiers with good accuracy and that are responsive to preference guarantees. |
| Researcher Affiliation | Academia | Berk Ustun 1 Yang Liu 2 David C. Parkes 1 1Harvard University, Cambridge, MA, USA 2UC Santa Cruz, Santa Cruz, CA, USA. |
| Pseudocode | Yes | Algorithm 1 Recursive Decoupling |
| Open Source Code | No | We provide software to reproduce our results at . The provided URL is a general project page (decoupled-classifiers.com) and not a direct link to a source-code repository. |
| Open Datasets | Yes | The datasets include: adult, the Adult dataset from the UCI ML Repository (Lichman, 2013); arrest and violent, the COMPAS recidivism dataset for arrest and violent crime (Angwin et al., 2016); apnea, a dataset to diagnose obstructive sleep apnea (Ustun et al., 2016); and cancer, a dataset to diagnose lung cancer (National Lung Screening Trial Research Team, 2011). |
| Dataset Splits | Yes | We allocate a third of the training data to the pruning procedure, and discard trees that violate rationality or envy-freeness at a significance level of 10%. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware specifications (e.g., GPU/CPU models, memory details) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'modern integer programming tools' but does not provide specific software dependencies with version numbers for replication. |
| Experiment Setup | Yes | We allocate a third of the training data to the pruning procedure, and discard trees that violate rationality or envy-freeness at a significance level of 10%. The final tree minimizes the worst-case group risk (see Section 2). |