Summary of Fairx: a Comprehensive Benchmarking Tool For Model Analysis Using Fairness, Utility, and Explainability, by Md Fahim Sikder et al.
FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability
by Md Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng, Fredrik Heintz
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary FairX, an open-source Python-based benchmarking tool, is designed to analyze models under the umbrella of fairness, utility, and explainability (XAI). The tool enables users to train bias-mitigation models and evaluate their fairness using various metrics, including fairness metrics, data utility metrics, and generate explanations for model predictions. FairX also supports the evaluation of synthetic data generated from fair generative models, as well as the training of such models. The tool’s library includes pre-processing, in-processing, and post-processing fair models, as well as custom datasets. FairX is publicly available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FairX is a special computer program that helps people make sure their artificial intelligence (AI) models are fair and work correctly. It’s like a referee for AI models! With FairX, you can test how well your model works on different kinds of data, see why it makes certain predictions, and even create new fake data that follows the same rules as real data. This helps make sure AI is used in a way that’s fair and good for everyone. |
Keywords
* Artificial intelligence * Synthetic data
