Intrinsic Bias

Intrinsic bias in machine learning models, particularly those processing text, images, and speech, refers to biases encoded within the model's internal representations, learned from biased training data. Current research focuses on developing methods to detect and mitigate this bias, employing techniques like embedding association tests, counterfactual interventions, and projective methods, often within the context of specific model architectures such as BERT and various transformer-based language models. Understanding and addressing intrinsic bias is crucial for ensuring fairness and reliability in AI applications across diverse domains, from clinical decision-making to social science research, as biased models can perpetuate and amplify existing societal inequalities. The development of robust bias detection and mitigation techniques is a significant area of ongoing investigation.