Summary of How Can We Diagnose and Treat Bias in Large Language Models For Clinical Decision-making?, by Kenza Benkirane et al.
How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making?
by Kenza Benkirane, Jackie Kay, Maria Perez-Ortiz
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent paper investigates the issue of bias in Large Language Models (LLMs) used for clinical decision-making, specifically focusing on gender and ethnicity biases. The authors introduce a novel dataset, Counterfactual Patient Variations (CPV), derived from JAMA Clinical Challenge, to evaluate and mitigate bias in LLMs. They propose a framework for bias evaluation using Multiple Choice Questions (MCQs) and corresponding explanations. Eight LLMs are prompted with fine-tuning as debiasing methods. The study finds that addressing social biases requires a multidimensional approach, as mitigating gender bias can occur while introducing ethnicity biases. Gender bias in LLM embeddings varies across medical specialities. Evaluating both MCQ response and explanation processes is crucial, as correct responses can be based on biased reasoning. The paper provides a framework for evaluating LLM bias, insights into the complex nature of bias, and strategies for mitigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are powerful tools that help doctors make decisions, but they can also be unfair to people from different genders and ethnicities. This research looks at how these models can be biased and what we can do to fix it. The scientists created a special dataset to test the models and found that making them fair is harder than it seems. They had to use multiple methods to make sure the models weren’t being unfair, and even then, they still found biases. This study shows that we need to be careful when using these models in real-life situations. |
Keywords
» Artificial intelligence » Fine tuning
