In most cases, it's quite well and, in some ways better than we know ourselves.

A study by AI experts at Brigham Young University, titled "Out of One, Many: Using Language Models to Simulate Human Samples," found that predictive AI programs exhibited a striking degree of what they call "algorithmic fidelity," or precise mapping to actual human behavior.

"Because these AI tools are basically trained on stuff that humans produce, things that we write, documents we make, websites we write, they can reflect back to us a lot of interesting and important things about ourselves," Ethan Busby, political psychologist and co-author of the study, told The Epoch Times. "Kind of like if someone read your diary from start to finish, they would know a lot of things about you, and you're not going to like every single thing.

"In a similar way," Busby said, "these tools have read so many things that humans have produced, and they can replicate or say back to us things about ourselves that we didn't necessarily know."

The study sought to analyze human behavior in the context of elections and asked how accurately a GPT-3 language model could predict voting patterns based on socio-demographic factors like a person's gender, age, location, religion, race, and economic status. The authors used these factors to create "silicon samples," or composite personas based on varying combinations of these attributes.