Targeted Bias Reduction in LLMs Can Worsen Unaddressed Biases

This ArXiv paper highlights a critical challenge in mitigating biases within large language models: focused bias reduction efforts can inadvertently worsen other, unaddressed biases. The research emphasizes the complex interplay of different biases and the potential for unintended consequences during the mitigation process.

• •Targeted bias mitigation strategies can unintentionally amplify existing biases.
• •Addressing one bias may create or worsen another, highlighting the interconnectedness of biases within LLMs.
• •This research underscores the need for comprehensive and holistic bias mitigation approaches.