Fair Multiple Decision Making Through Soft Interventions
Authors: Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using both synthetic and real-world datasets show the effectiveness of our approach. |
| Researcher Affiliation | Academia | Yaowei Hu University of Arkansas EMAIL Yongkai Wu Clemson University EMAIL Lu Zhang University of Arkansas EMAIL Xintao Wu University of Arkansas EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Reproducibility. The source code and datasets are available at https://github.com/yaoweihu/Fair-Multiple-Decision-Making. |
| Open Datasets | Yes | For the real-world data, we use the Adult dataset [19] and build the causal graph by using the PC algorithm implemented in the Tetrad [25]. Reproducibility. The source code and datasets are available at https://github.com/yaoweihu/Fair-Multiple-Decision-Making. |
| Dataset Splits | Yes | The dataset is randomly split to training and testing datasets. obtained from 5-fold cross-validation. |
| Hardware Specification | Yes | All experiments are conducted in a PC with 8GB RAM and Intel Core i5-1035G1 CPU. |
| Software Dependencies | No | The paper mentions software like CVXPY, PyTorch, and Adam optimizer, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | By default, we use 0.05 as the threshold for judging fairness. For the joint method, since the objective function and constraints are non-convex, we add constraints as penalty terms to the objective function and adopt Py Torch [22] to optimize it using the Adam optimizer. |