2023-10-25-JMP-Draft.pdf drive.google.com Abstract: In an era of rapid technological and social change, do universities adapt enough to play their important role in creating knowledge? To examine university adaptation, I extracted the information contained in the course catalogs of over 450 … Continued
Autoregressive language models, which use deep learning to produce human-like texts, have surged in prevalence. Despite advances in these models, concerns arise about their equity across diverse populations. While AI fairness is discussed widely, metrics to measure equity in dialogue … Continued
Complementing human decision-making with AI advice offers substantial advantages. However, humans do not always trust AI advice appropriately and are overly sensitive to incidental AI errors, even in cases with overall good performance. Today’s research still needs to uncover the … Continued
Exposure to large language model output is rapidly increasing. How will seeing AI-generated ideas affect human ideas? We conducted an experiment (800+ participants, 40+ countries) where participants viewed creative ideas that were from ChatGPT or prior experimental participants and then … Continued
Generative artificial intelligence (AI) is poised to reshape the way individuals communicate and interact. While this form of AI has the potential to efficiently make numerous human decisions, there is limited understanding of how individuals respond to its use in … Continued
We propose that individuals mistakenly interpret their success in predicting an event as an indication that they understand the underlying mechanism or process. In four experiments, we demonstrate this prediction-comprehension bias (PCB) and identify three factors that contribute to it: … Continued
osf.io We address the question of how humans select between multiple goals in a temporally-extended context. We probed whether humans engage in the prospec- tive evaluation of goals by estimating which goals are likely to yield future success and choosing … Continued
While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a procedure that uses machine learning algorithms—and their capacity to notice patterns people might not—to generate novel hypotheses about human behavior. We illustrate the procedure … Continued
AI assistance in decision-making has become popular, yet people’s inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, through three pre-registered, randomized human subject experiments, we explore whether and how the provision of {second opinions} … Continued
The emergence of unveiling human-like behaviors in Large Language Models (LLMs) has led to a closer connection between NLP and human psychology, leading to a proliferation of computational agents. Scholars have been studying the inherent personalities displayed by LLM agents … Continued