Representation Bias

Representation bias in machine learning refers to systematic inaccuracies in models arising from skewed or incomplete training data, leading to unfair or inaccurate predictions for certain groups. Current research focuses on identifying and mitigating these biases across various model architectures, including large language models and multimodal systems, employing techniques like data balancing, adversarial reweighting, and counterfactual augmentation. Understanding and addressing representation bias is crucial for ensuring fairness and accuracy in AI applications, impacting fields ranging from facial recognition and image generation to healthcare and information retrieval. The ultimate goal is to develop models that generalize well to all populations, avoiding the perpetuation of societal stereotypes and inequalities.