Biased Prediction

Biased prediction in machine learning focuses on identifying and mitigating unfair outcomes produced by algorithms trained on biased data, aiming to ensure equitable predictions across different demographic groups. Current research emphasizes developing methods to debias data and models, employing techniques like adversarial learning, counterfactual reasoning, and customized loss functions within various architectures including neural networks and graph neural networks. This work is crucial for addressing ethical concerns and ensuring fairness in high-stakes applications like healthcare, criminal justice, and climate modeling, ultimately promoting responsible AI development and deployment.