FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability

Unified Framework Simplifies Fairness Testing: FairX provides a single tool that combines fairness metrics, utility metrics, and explainability analysis, allowing organizations to comprehensively evaluate AI models without needing multiple separate tools or platforms.

Addresses Gap in Fair Synthetic Data Evaluation: Unlike existing benchmarking tools, FairX can both train fair generative models and evaluate the quality of synthetic data they produce, which is important as organizations increasingly use synthetic data for training and testing AI systems.

Supports Multiple Bias-Mitigation Approaches: The tool includes fair models using three different strategies (pre-processing, in-processing, and post-processing), enabling users to test which approach works best for their specific use case rather than being limited to a single method.

Accessible to Non-Specialists: As an open-source Python tool, FairX makes fairness and explainability analysis more accessible to practitioners who may not have deep technical expertise in bias mitigation, democratizing responsible AI practices.

Flexible for Real-World Applications: FairX supports both tabular and image datasets and allows users to upload custom datasets, making it practical for evaluating fairness across different types of AI systems that organizations actually deploy.