Opinion: AI-driven data analysis could exacerbate misaligned incentives in biomedical research
Rapid, complex, and technically sound data analysis is insufficient — and sometimes antithetical — to the generation of real knowledge.
This summer OpenAI released Code Interpreter, a plug-in for the popular ChatGPT tool that allows it to take in datasets, write and run Python code, and “create charts, edit files, perform math, etc.“ It aims to be nothing short of the ideal statistical collaborator or research software engineer, providing the necessary skill and speed to overcome the limitations of one’s research program at a fraction of the price.
It is a bad omen, then, that while statisticians are known for pestering researchers with difficult but important questions like “What are we even trying to learn?”, Code Interpreter responds to even half-baked requests with a cheerful “Sure, I’d be happy to.” There are risks to working with a collaborator that has both extraordinary efficiency and an unmatched desire to please.
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