Debiasing Framework

Debiasing frameworks aim to mitigate biases in machine learning models, particularly large language models (LLMs) and vision-language models, which often perpetuate societal inequalities by reflecting biases present in their training data. Current research focuses on developing techniques like multi-LLM approaches, prompt engineering, and adversarial learning to neutralize biases related to protected attributes (gender, race, age) without sacrificing model performance. These advancements are crucial for ensuring fairness and ethical considerations in AI applications, impacting fields ranging from healthcare and finance to social sciences, where biased models can lead to discriminatory outcomes.