Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
FAHT: An Adaptive Fairness-aware Decision Tree Classifier
Authors: Wenbin Zhang, Eirini Ntoutsi
IJCAI 2019 | Venue PDF | 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. |