Evaluating and Mitigating Discrimination in Language Model Decisions

This post introduces a method for proactively evaluating and mitigating discrimination in language model decisions. It details a process where an LM generates diverse prompts across 70 societal decision scenarios, systematically varying demographic information. This methodology revealed patterns of both positive and negative discrimination in Claude 2.0 without interventions. The post demonstrates techniques, primarily through careful prompt engineering, to significantly decrease discrimination, providing pathways for safer deployment in appropriate use cases. A dataset and prompts are released for further research.