Bias Related Issue

Bias in artificial intelligence, particularly in machine learning models, is a significant research area focused on identifying and mitigating unfair or discriminatory outcomes. Current research investigates bias detection and mitigation techniques across various model architectures, including deep neural networks and large language models, employing methods such as fairness metrics, causal inference, and explainable AI techniques to analyze and address biases stemming from training data and model architecture. This work is crucial for ensuring fairness and trustworthiness in AI systems across diverse applications, ranging from visual recognition to natural language processing, and impacts the development of more equitable and responsible AI technologies.