Awesome Fairness in AI
A curated list of resources for understanding, measuring, and mitigating fairness issues in artificial intelligence and machine learning systems.
What is Awesome Fairness in AI?
Awesome-Fairness-in-AI is a curated collection of resources dedicated to fairness in artificial intelligence. It compiles academic papers, tools, and frameworks that help researchers and practitioners understand, measure, and mitigate algorithmic bias in machine learning systems. The project addresses the problem of AI systems exhibiting discrimination in high-stakes applications that affect individual lives.
AI researchers, data scientists, machine learning engineers, and ethicists who are working on or studying algorithmic fairness, bias detection, and mitigation strategies in artificial intelligence systems.
It provides a centralized, organized repository of the most relevant fairness resources, saving researchers time from scouring disparate sources. Unlike generic AI resource lists, it focuses specifically on fairness literature and tools, making it the go-to reference for this emerging subfield.
Overview
A curated list of awesome Fairness in AI resources
Use Cases
Best For
- Finding foundational papers on fairness definitions and measurements
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