Causal Fairness for Machine Learning
Addressing AI bias through causal fairness, emphasizing the importance of causal effects in measuring and mitigating bias
AI bias is a critical challenge in modern technology. Our team addresses this issue by focusing on causal fairness, emphasizing the importance of causal effects in measuring and mitigating bias in AI systems. Through pioneering research in causal and counterfactual fairness, we ensure AI models produce equitable outcomes by evaluating counterfactual scenarios. In collaboration with the University of Arkansas, we are expanding this research to explore causal fairness in dynamic and non-iid (non-independent and identically distributed) settings, advancing fairness research in complex and evolving environments.