Achieving Long-Term Fairness in Sequential Decision Making
Authors: Yaowei Hu, Lu Zhang9549-9557
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical evaluation shows the effectiveness of the proposed algorithm on synthetic and semi-synthetic temporal datasets. |
| Researcher Affiliation | Academia | Yaowei Hu, Lu Zhang University of Arkansas {yaoweihu, lz006}@uark.edu |
| Pseudocode | Yes | Algorithm 1: Repeated Risk Minimization |
| Open Source Code | Yes | The code and hyperparameter settings are available online: https://github.com/yaoweihu/Achieving-Long-term-Fairness. |
| Open Datasets | Yes | Semi-synthetic Data. We use the Taiwan credit card dataset (Yeh and Lien 2009) as the initial data at t = 1. |
| Dataset Splits | No | The paper describes a 'training process' but does not explicitly provide details about train/validation/test dataset splits, specific percentages, or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like PyTorch and CVXPY but does not provide specific version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | The code and hyperparameter settings are available online: https://github.com/yaoweihu/Achieving-Long-term-Fairness. For our algorithm, we use the logistic loss function for the surrogate function ϕ and the linear model for the decision model. All algorithms use the l2-regularization which can equip the logistic loss function with strong convexity. In our algorithm, Re LU activation function is adopted to ensure that the fairness constraints are always non-negative, and we adopt Py Torch (Paszke et al. 2019) to implement optimization with Adam optimizer. |