Auditing Bias in Large Language Models

How do you analyze a large language model (LLM) for harmful biases? The 2022 release of ChatGPT launched LLMs onto the public stage. Applications that use LLMs are suddenly everywhere, from customer service chatbots to LLM-powered healthcare agents. Despite this widespread use, concerns persist about bias and toxicity in LLMs, especially with respect to protected characteristics such as race and gender.  In this blog post, we discuss our recent research that uses a role-playing scenario to audit ChatGPT, an approach that opens new possibilities for revealing unwanted biases. At the SEI, we’re working to understand and measure the trustworthiness of artificial intelligence (AI) systems. When harmful bias is present in LLMs, it can decrease the trustworthiness of the technology and limit the use cases for which the technology is appropriate, making adoption more difficult. The more we understand how to audit LLMs, the better equipped we are to identify and address learned biases.