awesome-fairness-in-ai and AI_fairness
These are ecosystem siblings—one is a curated index/discovery resource for fairness-in-AI tools and methodologies, while the other is a standalone repository providing direct implementations of bias detection and mitigation techniques that would itself be catalogued within such a collection.
About awesome-fairness-in-ai
datamllab/awesome-fairness-in-ai
A curated list of awesome Fairness in AI resources
This resource helps anyone developing or deploying AI systems ensure their models are fair and unbiased. It provides a curated list of research papers and resources covering various aspects of algorithmic fairness, from theoretical understandings and fairness measurements to bias detection and mitigation strategies. Researchers, data scientists, and AI ethicists can use this to understand, identify, and reduce discrimination in AI.
About AI_fairness
Ali-Alameer/AI_fairness
This GitHub repository offers resources to create fair and unbiased AI systems, including libraries, tools and tutorials on identifying and mitigating bias in machine learning models and implementing fairness in AI.
Building AI systems that are fair and unbiased is critical for many organizations. This project provides libraries, tools, and tutorials to help you identify and reduce bias in your machine learning models, ensuring more equitable outcomes. It's designed for AI developers and researchers who are responsible for the ethical implementation of AI.
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