Tools for addressing Fairness in ML
- Fairmodels: Tool for bias detection, visualization, and mitigation.
- FAT Forensics: Python toolkit for evaluating Fairness, Accountability and Transparency of AI systems
- AI Fairness 360: IBM Research Trusted AI. Open source toolkit for examining, reporting, and mitigating discrimination and bias in ML models.
Some Venues on Explainable, Fair & Trustworthy AI
- ACM FAccT 2021: ACM Conference on Fairness, Accountability, and Transparency
- AIES 2021: AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society
- FORC 2021: Symposium on Foundations of Responsible Computing
- BIAS 2021: International Workshop on Algorithmic Bias in Search and Recommendation
- XKDD 2020: ECML-PKDD Workshop on eXplainable Knowledge Discovery in Data Mining
- TrustML: Bi-weekly Seminar Series of The Trustworthy ML Initiative