We initially doubted the ability of LLMs to capture human judgments, but as we detail in Box 1, the moral judgments of GPT-3.5 were extremely well aligned with human moral judgments in our analysis (r=0.95; full details at https://nikett.github.io/gpt-as-participant). Human morality is often argued to be especially difficult for language models to capture, and yet we found powerful alignment between GPT-3.5 and human judgments.

We emphasize that this finding is just one anecdote, and we do not make any strong claims about the extent to which LLMs make human-like judgments, moral or otherwise. Language models might also be especially good at predicting moral judgments because moral judgments heavily hinge on the structural features of scenarios, including the presence of an intentional agent, the causation of damage, and a vulnerable victim – features that language models may have an easy time detecting. However, the results are intriguing.

Other researchers have empirically demonstrated GPT-3’s ability to simulate human participants in domains beyond moral judgments, including predicting voting choices, replicating behavior in economic games, and displaying human-like problem solving and heuristic judgments on scenarios from cognitive psychology. LLM studies have also replicated classic social science findings, including the Ultimatum Game and the Milgram experiment. One company (http://syntheticusers.com) is expanding on these findings, building infrastructure to replace human participants and offering ‘synthetic AI participants’ for studies.

From Caveats and looking ahead

Language models may be far from human, but they are trained on a tremendous corpus of human expression, and thus they could help us learn about human judgments. We encourage scientists to compare simulated language model data with human data to see how aligned they are across different domains and populations. Just as language models like GPT may help to give insight into human judgments, comparing LLMs with human judgments can teach us about the machine minds of LLMs; for example, shedding light on their ethical decision making.

Lurking under the specific concerns about the usefulness of AI language models as participants is an age-old question: can AI ever be human enough to replace humans? On the one hand, critics might argue that AI participants lack the rationality of humans, making judgments that are odd, unreliable, or biased. On the other hand, humans are odd, unreliable, and biased – and other critics might argue that AI is just too sensible, reliable, and impartial. What is the right mix of rational and irrational to best capture a human participant? Perhaps we should ask a big sample of human participants to answer that question. We could also ask GPT.