Multiple Bias

Multiple bias in large language models (LLMs) and other AI systems is a growing concern, focusing on how these systems perpetuate and amplify existing societal inequalities across various dimensions like gender, race, and socioeconomic status. Current research emphasizes developing and evaluating bias detection and mitigation techniques, including novel metrics like allocational bias indices and multi-faceted debiasing algorithms that address intersecting biases simultaneously, often leveraging counterfactual data augmentation and structured knowledge. This work is crucial for ensuring fairness and reliability in AI applications impacting high-stakes decisions, promoting responsible AI development, and advancing our understanding of how algorithmic bias interacts with social structures.