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..
Achieving Long-Term Fairness in Sequential Decision Making
Authors: Yaowei Hu, Lu Zhang9549-9557
AAAI 2022 | Venue PDF | 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 EMAIL |
| 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. |