Achieving Causal Fairness through Generative Adversarial Networks
Authors: Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a real-world dataset show that CFGAN can generate high quality fair data. |
| Researcher Affiliation | Academia | Depeng Xu , Yongkai Wu , Shuhan Yuan , Lu Zhang and Xintao Wu University of Arkansas {depengxu,yw009,sy005,lz006,xintaowu}@uark.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | Yes | The dataset we use for evaluation is the UCI Adult income dataset [Dheeru and Karra Taniskidou, 2017]. |
| Dataset Splits | No | The paper mentions using the UCI Adult dataset but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or a citation to a predefined split). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software components with their version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | The fairness threshold is 0.05, i.e., the effect should be in [ 0.05, 0.05] to be fair. [...] λ is a hyperparameter which controls a trade-off between utility and fairness of data generation. [...] Figure 5 shows the results for total effect, where we get a fairly good trade-off between utility and fairness at λ = 1. We evaluate 4 classifiers: support vector machine (SVM), decision tree (DT), logistic regression (LR) and random forest (RF). |