Can SAEs reveal and mitigate racial biases of LLMs in healthcare?

Large language models (LLMs) hold significant promise in healthcare by potentially reducing physicians' workload and enhancing patient care (F1). Despite these benefits, there are ongoing concerns regarding biases embedded within these models, which could lead to unfair or inaccurate outcomes (F2). Recent exploratory research suggests that Sparse Autoencoders (SAEs) may play a crucial role in addressing these issues by revealing and mitigating racial biases present in LLMs used for healthcare applications (F3, A1). By leveraging SAEs, it may be possible to ensure that predictions made by LLMs are fairer and more equitable across diverse patient populations. This approach highlights a proactive step toward improving the reliability and ethical deployment of AI technologies in medical settings. While the findings remain exploratory, they underscore the importance of continued investigation into bias detection and correction methods within AI systems. Overall, integrating SAEs could contribute to more trustworthy and inclusive healthcare AI solutions.