Potential Bias

Potential bias in machine learning models is a critical research area aiming to identify and mitigate unfair or discriminatory outcomes stemming from biased training data or algorithmic design. Current research focuses on developing methods to detect bias across various model architectures, including deep neural networks and large language models, often employing techniques like multi-objective optimization, adversarial training, and bias-aware loss functions to improve fairness without sacrificing accuracy. This work is crucial for ensuring the ethical and responsible deployment of AI systems across diverse applications, ranging from medical diagnosis to legal decision-making, and promoting equitable outcomes for all users.