ReLax: Efficient and Scalable Recourse ExplanationBenchmarking using JAX

From healthcare to criminal justice, machine learning (ML) models have permeated society to support domain experts in making decisions.Given the high-stakes nature of decision outcomes in some real-world domains, concerns over the trustworthiness of ML model predictions have raised increasing attention.These concerns have spurred surging research interests in explainable artificial intelligence (XAI), whose mission is to equip end-users with an understanding (or explanation) of AI decision making, and to help provide assessments to end-users about when to rely on ML models and when to exercise caution.