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Recent Peer Reviewed Journal Articles & Conference Proceedings

Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications,  Baseman et al., 2023, Journal of Diabetes Science and Technology.

Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies

Large language models are touted as approaching human-like performance on a variety of human intelligence tasks. But are LLMs really “performing” this well?

In my lab we are interested in putting this question to a systematic analysis. To address this challenge, we introduce the Turing Experiment methodology. Unlike Turing’s Imitation Game, which involves simulating a single arbitrary individual, a Turing Experiment requires simulating a diverse sample of participants in human subject research and determining how well the simulation results align with human results. 

Initial findings from this human-centered approach show that Turing Experiments can replicate experimental findings, show sensitivity to group differences, and identify distortions. 

Today, a major risk in replacing human studies with simulations is that the simulations might be reflecting biases from the authors of the model training data rather than the actual behavior of human populations.

Thus, comparing Turing Experiments results to empirical human results can be useful in identifying these distortions. In the long-run, language model-based simulations may be a useful alternative when it is costly to carry out experiments on humans due to scale, selection bias, monetary cost, legality, morality, or privacy. They can also lead to AI approaches that meet the needs of groups of humans as opposed to taking a one-model fits all approach.