Public Understanding of Artificial Intelligence through Entertainment Media
Principal Investigator(s): View help for Principal Investigator(s) Samuel Baker, University of Texas-Austin; Suzanne Scott, University of Texas-Austin; Paul Toprac, University of Texas-Austin
Version: View help for Version V1
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Project Citation:
Baker, Samuel, Scott, Suzanne, and Toprac, Paul. Public Understanding of Artificial Intelligence through Entertainment Media. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-10-15. https://doi.org/10.3886/E152542V1
Project Description
Summary:
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Our data was collected for a project on how media representations shape public perceptions of AI and then use what we learn to explore how we might better represent everyday interactions with AI to the public. We began by developing lists, taxonomies, and case studies of popular representations of AI and hypotheses about how the general public and elite groups discriminate between “good” and “bad” AI, and about how media representations help shape these perceptions. Our research questions include:
- What drives popular negativity about AI? Does the public have its reasons that the experts know not of? Or have the public adopted views of AI borne of misrepresentations?
- Do overtly dystopian representations of AI feed, or perhaps temper, public outrage about insidious issues with AI and machine learning such as the biases of search algorithms?
- Can we produce narratives on AI in different media modalities that are more nuanced and complex than just the false dichotomy of good and bad? Could a more accurately critical (and yet still exciting) model of AI themed entertainment be developed, once we’ve gained an understanding of how the public has been encountering AI?
Funding Sources:
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University of Texas-Austin (This work was supported by Good Systems, a research grand challenge at the University of Texas at Austin.)
Scope of Project
Subject Terms:
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Entertainment Media;
Artificial Intelligence;
Public Perception
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