Semantic Bias

Semantic bias in AI models, encompassing systematic errors stemming from skewed training data or inherent model architectures, is a significant area of research. Current efforts focus on mitigating these biases through techniques like incorporating causal analysis and adversarial learning to identify and neutralize confounding factors, refining feature learning with category prompts to improve representation of underrepresented classes, and addressing biases related to specific linguistic structures or data formatting conventions. Understanding and addressing semantic bias is crucial for building more reliable and equitable AI systems across diverse applications, ranging from image classification and natural language processing to speech recognition.

Papers