Pushing Back on AI: A Dialogue with ChatGPT on Causal Inference in Epidemiology

To close this book with a view toward the future, this chapter present a Socratic dialogue with ChatGPT, a large language model (LLM), about the causal interpretation of epidemiological associations between fine particulate matter (PM2.5) and human mortality risks. ChatGPT and similar AI conversational systems reflect common patterns of human reasoning and argumentation in the sources on which they have been trained. In the example in this chapter, ChatGPT initially holds that “It is well-established that exposure to ambient levels of PM2.5 does increase mortality risk” and adds the unsolicited admonishment that “Reducing exposure to PM2.5 is an important public health priority.” After patient questioning, however, it concludes that “It is not known with certainty that current ambient levels of PM2.5 increase mortality risk. While there is strong evidence of an association between PM2.5 and mortality risk, the causal nature of this association remains uncertain due to the possibility of omitted confounders.” This revised evaluation of the evidence suggests the potential value of sustained questioning in refining and improving both the types of human reasoning and argumentation imitated by current LLMs and the reliability of the initial conclusions expressed by current LLMs.