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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 18·5 min read
Demographic Injection in Medical Language Models under Diversity, Equity, and Inclusion Prompts
TL;DR
Demographic injection occurs when medical language models, prompted with diversity, equity, and inclusion (DEI) statements, inaccurately add patient demographic details not present in the original question. This leads to a significant increase in misrepresentation, with injection rates rising from 0.7% to 33.1% across 47 models.
✦ Why It Matters
Review and refine DEI prompts to avoid unintended demographic assumptions in AI-generated medical responses.
Key Takeaways
- Evaluate the impact of DEI prompts on model outputs.
- Implement safeguards against demographic injection in AI systems.
- Train models with diverse datasets to minimize bias.
- Regularly audit AI-generated medical recommendations for accuracy.
How It Works
Demographic injection occurs when language models, influenced by DEI prompts, generate demographic details that were not part of the original input. This happens because the models are designed to respond to prompts by inferring context, which can lead to the addition of unrequested attributes.