RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation

1. 1Install RecourseBench using pip to utilize its modular framework
2. 2Configure the framework to integrate with existing algorithmic recourse methods
3. 3Run experiments using RecourseBench to evaluate and compare different methods
4. 4Apply systematic verification to ensure integrated methods reproduce originally reported results
5. 5Test the framework's interoperability with various machine learning models and datasets

Data scientists and machine learning engineers can use RecourseBench to compare and evaluate different algorithmic recourse methods, ensuring reproducibility and fairness in their models

💡 RecourseBench enables principled comparison of algorithmic recourse methods, ensuring reproducibility and fairness in machine learning models

🚀 Introducing RecourseBench: a modular framework for reproducible algorithmic recourse evaluation 🚀