This research reveals that common prompt-based debiasing methods often create only a superficial appearance of bias reduction in large language models.

• Models like Llama2-7B-Chat frequently misclassify biased content despite debiasing prompts
• Current debiasing approaches assume LLMs inherently understand bias - an assumption this study challenges
• Systematic analysis across multiple benchmarks (BBQ, StereoSet) and various models exposes effectiveness gaps
• Security implications are significant as seemingly "debiased" systems may still perpetuate harmful stereotypes

These findings highlight the need for more robust debiasing methods beyond simple prompting to build truly trustworthy AI systems for enterprise deployment.

Rethinking Prompt-based Debiasing in Large Language Models