Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

The reel deal? An experimental analysis of perception bias and AI film pitches

Bibliographic Data

ID21579031
AuthorsPaul Crosby (0000-0001-8821-7463, Macquarie University), Jordi Mckenzie (0000-0003-4081-6189, Macquarie University, corresponding author)
Year2025
Volume49
Issue2
Pages281-300
Publication date2025-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Cultural Economics (JOURNAL)
Journal identifiersISSN: 0885-2545 • E-ISSN: 1573-6997
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s10824-025-09534-4
OpenAlexW4407668509
LanguageEN
Citations received1
References cited12

Artificial intelligence (AI) is creating significant disruption in many creative industries, including the film industry. With increase in levels of investment in sophisticated AI technologies, film studios can potentially replace, or at least reduce, traditional roles of film professionals. Not surprisingly, such moves have been met with vocal opposition from industry associations and labor unions. In the discourse of such debate, however, limited attention has been given to consumer acceptance of AI in filmmaking. This study provides the first evidence concerning potential perception biases in the context of AI film development, related to film ‘pitches.’ Using a randomized experiment of 500 participants, we find no discernible bias related to AI-generated synopses, inclusive of AI-generated director and casting decisions. Our results suggest that consumers may be accepting of, at least, limited involvement of AI in film development

Advertising · Art · Business · Cultural economics · Econometrics · Economics · Perception · Reel · The arts · Visual arts · Aesthetic Perception and Analysis · Artificial Intelligence in Games · Cinema and Media Studies · Psychology

  • Talent and technology in creative industries

    Open Access•Ricard Gil, S Abraham Ravid et al.•Journal of Cultural Economics•2025

  • Are you ready for artificial Mozart and Skrillex? An experiment testing expectancy violation theory and AI music

    Open Access•Joo-Wha Hong, Qiyao Peng et al.•New Media & Society•2020

Unique citing works1
Citations per year1
Citation span2025 - 2025 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 1

Tools

Open DOIOpen Access
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae