Achieving Non-Discrimination in Prediction
Authors: Lu Zhang, Yongkai Wu, Xintao Wu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments demonstrate the theoretical results and show the effectiveness of our two-phase framework. |
| Researcher Affiliation | Academia | Lu Zhang, Yongkai Wu, and Xintao Wu University of Arkansas {lz006,yw009,xintaowu}@uark.edu |
| Pseudocode | Yes | Algorithm 1: Two-phase framework. 1 If DED+εh,D τ, we are done. Otherwise, modify the labels in the training dataset D to obtain a modified dataset D such that |DED | τ. 2 Train a classifier h on D . If DED +εh ,D τ, we are done. Otherwise, tweak classifier h to meet the above requirement. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We first learn a causal model M for a real dataset, the Adult dataset [Lichman, 2013]... [Lichman, 2013] M. Lichman. UCI machine learning repository. http://archive.ics.uci.edu/ml, 2013. |
| Dataset Splits | No | The paper uses 'training data' but does not specify explicit train/validation/test splits, percentages, or cross-validation details for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or processing units) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Tetrad' and 'decision tree (DT) and support vector machine (SVM)' but does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | No | The paper mentions the discrimination threshold (τ = 0.05) and algorithms used, but does not specify concrete hyperparameters or detailed training configurations (e.g., learning rates, batch sizes, specific DT/SVM parameters) for the models. |