FAHT: An Adaptive Fairness-aware Decision Tree Classifier
Authors: Wenbin Zhang, Eirini Ntoutsi
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our algorithm is able to deal with discrimination in streaming environments, while maintaining a moderate predictive performance over the stream. The first goal of our experiments is to evaluate the predictivevs fairness-performance of our proposed FAHT method, as is typical in the domain of fairness-aware machine learning [Verma and Rubin, 2018]. To this end, we evaluate the different models in terms of accuracy and statistical parity. |
| Researcher Affiliation | Academia | Wenbin Zhang1 and Eirini Ntoutsi2 1University of Maryland, Baltimore County, MD 21250, USA 2Leibniz University Hannover, 30167, Hannover, Germany |
| Pseudocode | No | The paper describes the FAHT algorithm in detail in Section 4 but does not include a formal pseudocode or algorithm block. |
| Open Source Code | Yes | The entire experimental results and code are available at https://github.com/vanban Truong/FAHT |
| Open Datasets | Yes | Among the available datasets, the ones that best meet our requirements are the Adult and the Census datasets [Dheeru and Karra Taniskidou, 2017] |
| Dataset Splits | No | The paper describes using 'prequential evaluation' for streaming data, where each instance is first predicted and then used for model update ('first-test-then-train'). It does not specify fixed train/validation/test splits as one would for a static dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers. |
| Experiment Setup | Yes | The Adult data stream is processed in sliding windows; Each window trains a base learner as the ensemble component, the oldest one will be replaced when the classifier window is full; The ensemble members stored in the classifier window will also get updated with the instances in the current sliding window. when the window size is 1000. |