Individually Fair Gradient Boosting
Authors: Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate the efficacy of our algorithm on three ML problems susceptible to algorithmic bias. |
| Researcher Affiliation | Collaboration | Alexander Vargo Department of Mathematics University of Michigan ahsvargo@umich.edu Fan Zhang School of Information Science and Technology Shanghai Tech University zhangfan4@shanghaitech.edu.cn Mikhail Yurochkin IBM Research MIT-IBM Watson AI Lab mikhail.yurochkin@ibm.com Yuekai Sun Department of Statistics University of Michigan yuekai@umich.edu |
| Pseudocode | Yes | Algorithm 1 Fair gradient boosting |
| Open Source Code | No | No explicit statement of releasing code or a link to a code repository for the described methodology was found. |
| Open Datasets | Yes | The German credit data set (Dua & Graff, 2017) contains information from 1000 individuals; the ML task is to label the individuals as good or bad credit risks. The Adult data set (Dua & Graff, 2017) is another common benchmark in the fairness literature. We study the COMPAS recidivism prediction data set (Larson et al., 2016). |
| Dataset Splits | Yes | For the baseline GBDT and projecting, we select hyperparameters by splitting 20% of the training data into a validation set and evaluating the performance on the validation set. |
| Hardware Specification | No | Section C.6 mentions the number of CPUs and GPUs (e.g., "4 CPUs", "1 GPU") used for experiments but does not provide specific models, types, or memory details of the hardware. |
| Software Dependencies | Yes | We use the default parameters in the Ridge CV class from the scikit-learn package, version 0.21.3 (Pedregosa et al., 2011). We implement the adversarial debiasing methods (in the adversarial_debiasing class from the IBM’s AIF360 package, version 0.2.2 (Bellamy et al., 2018)). |
| Experiment Setup | Yes | Table 4: Optimal XGBoost parameters for German credit data set. For Bu DRO, we also used a pertubation budget of ϵ = 1.0. Table 6: Optimal XGBoost parameters for the Adult data set. Table 8: Optimal XGBoost parameters for COMPAS data set. |