When working with generative artificial intelligence (AI), users may see productivity gains, but the AI-generated content may not match their preferences exactly. To study this effect, we introduce a Bayesian framework in which heterogeneous users choose how much information to share with the AI, facing a trade-off between output fidelity and communication cost. We show that the interplay between these individual-level decisions and AI training may lead to societal challenges. Outputs may become more homogenized, especially when the AI is trained on AI-generated content. And any AI bias may become societal bias. A solution to the homogenization and bias issues is to improve human-AI interactions, enabling personalized outputs without sacrificing productivity.
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Ryan Watkins, Ph.D.
▲ Professor, George Washington University
▲ Professor, George Washington University
Program Director: Educational Technology Leadership MA Program
www.RyanRWatkins.com (resume, books, articles, etc.)
www.LLMinScience.com (hub for using LLMs in research)
SciencePods (create a podcast about your research)
go.gwu.edu/CodingProjects (coding projects by discipline)
www.WeShareScience.com (an online video science fair)
go.gwu.edu/GWcoders (weekly meet up for connecting with others who code)
www.NeedsAssessment.org (all things needs assessment)
www.NeedsAssessment.org (all things needs assessment)
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Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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