Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate
Yuancheng Xu* · Chenghao Deng* · Yanchao Sun · Ruijie Zheng · Xiyao Wang · Jieyu Zhao · Furong Huang
The codebase for the paper "Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making" (https://arxiv.org/abs/2309.03426) by Yuancheng Xu*, Chenghao Deng*, Yanchao Sun, Ruijie Zheng, Xiyao Wang, Jieyu Zhao, Furong Huang.
The implementation of ELBERT Policy Optimization (ELBERT-PO) is provided.
First, install Anaconda to set up virtual environment. Then, run:
conda env create -f elbert.yaml
conda activate elbert
pip install -r requirements.txt
The .scripts/ folder includes bash scripts for ELBERT and other baselines (G-PPO, R-PPO, A-PPO) in five enviroments:
- ELBERT
bash scripts/lending_elbert.sh
- Baseline (G-PPO, R-PPO, A-PPO)
bash scripts/lending_original.sh
- ELBERT
bash scripts/infectious_original_env_elbert.sh
- Baseline (G-PPO, R-PPO, A-PPO)
bash scripts/infectious_original_env_original.sh
- ELBERT
bash scripts/infectious_harder_env_elbert.sh
- Baseline (G-PPO, R-PPO, A-PPO)
bash scripts/infectious_harder_env_original.sh
- ELBERT
bash scripts/attention_original_env_elbert.sh
- Baseline (G-PPO, R-PPO, A-PPO)
bash scripts/attention_original_env_original.sh
- ELBERT
bash scripts/attention_harder_env_elbert.sh
- Baseline (G-PPO, R-PPO, A-PPO)
bash scripts/attention_harder_env_original.sh
Our codebase is based on the following repo:
Thanks for their efforts to make the code public!
Please cite our work if you find it is helpful:
@inproceedings{xuadapting,
title={Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate},
author={Xu, Yuancheng and Deng, Chenghao and Sun, Yanchao and Zheng, Ruijie and Wang, Xiyao and Zhao, Jieyu and Huang, Furong},
booktitle={Forty-first International Conference on Machine Learning}
}