7 papers
Predictive AI Can Support Human Learning while Preserving Error Diversity
Vivianna Fang He, Sihan Li, Phanish Puranam +1
We examined the effects of predictive AI deployment on the immediate performance and learning of medical novices. In two pre-registered field experiments, we varied whether AI inpu…
Why They Disagree: Decoding Differences in Opinions about AI Risk on the Lex Fridman Podcast
Nghi Truong, Phanish Puranam, Ãzgecan Koçak
The emergence of transformative technologies often surfaces deep societal divisions, nowhere more evident than in contemporary debates about artificial intelligence (AI). A strikin…
Thinking with Many Minds: Using Large Language Models for Multi-Perspective Problem-Solving
Sanghyun Park, Boris Maciejovsky, Phanish Puranam
Complex problem-solving requires cognitive flexibility--the capacity to entertain multiple perspectives while preserving their distinctiveness. This flexibility replicates the "wis…
How Generative AI Adoption Alters the Demand for Cognitive and Social Skills Within Roles: A Skill-Centric Analysis
Piyush Gulati, Arianna Marchetti, Phanish Puranam +1
A common view holds that generative AI (GenAI) automates cognitive tasks, reshaping roles to emphasize social skills over cognitive ones. Drawing on the framework we develop in thi…
Can LLMs Help Improve Analogical Reasoning For Strategic Decisions? Experimental Evidence from Humans and GPT-4
Phanish Puranam, Prothit Sen, Maciej Workiewicz
This study investigates whether large language models, specifically GPT4, can match human capabilities in analogical reasoning within strategic decision making contexts. Using a no…
Why Trust in AI May Be Inevitable
Nghi Truong, Phanish Puranam, Ilia Testlin
In human-AI interactions, explanation is widely seen as necessary for enabling trust in AI systems. We argue that trust, however, may be a pre-requisite because explanation is some…