Dataset Information

Who Gets Credit for AI-Generated Art?

ABSTRACT: The recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system and credit to different stakeholders involved in art production. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI-as a tool versus agent-with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems and offer an empirical lens to reason about how to allocate credit and responsibility to human stakeholders.

SUBMITTER: Epstein Z

PROVIDER: S-EPMC7492988 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Who Gets Credit for AI-Generated Art?

Epstein Ziv Z Levine Sydney S Rand David G DG Rahwan Iyad I

iScience 20200829 9

The recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated w ...[more]

PMID: 32920489

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Project description:The transformation of social development modes has led to profound changes in the pattern of intangible cultural heritage, while simultaneously posing significant challenges to its preservation. The rapid development of artificial intelligence (AI) technology has brought new development opportunities in various research fields. This study intends, by constructing and evaluating a theoretical model, to investigate whether AI-generated cultural and creative products can promote the sustainability of intangible cultural heritage. The central focus of this research is to measure the effectiveness of AI technologies in promoting the sustainability of intangible cultural heritage. The context of the research design is rooted in the attention, interest, search, action, and share (AISAS) model, incorporating theories of perceived value and cultural identity, to forecast the long-term viability of AI-generated cultural and creative products in the promotion of intangible cultural heritage. This research was conducted in Tianjin, China and carried out using quantitative methods, a questionnaire survey, and the accidental sampling method, taking a sample of 291 participants for analysis. The results show that 1) the attraction of and interest and participation in AI-generated Yangliuqing New Year Print cultural and creative products have a positive effect on perceived value; 2) the purchase and sharing of these products have a positive impact on cultural identity; 3) the perceived value has a positive impact on cultural identity; and 4) cultural identity has a positive impact on the sustainability of intangible cultural heritage. This study contributes to the theoretical development and practical application of the AISAS model and offers valuable insights into the future development trajectory of intangible cultural heritage, thereby promoting its sustainability. The limitations of this study are its small sample size and geographical restrictions. In future studies, the sample size will be expanded and will include more regions for data analysis.

Project description:Although fluency theory predominates psychological research on human aesthetics, its most severe limitation may be to explain why art that challenges or even violates easy processing can nevertheless be aesthetically rewarding. We discuss long-standing notions on art's potential to offer mental growth opportunities and to tap into a basic epistemic predisposition that hint at a fluency counteracting aesthetic pleasure mechanism. Based on divergent strands of literature on empirical, evolutionary, and philosophical aesthetics, as well as research on disfluency, we presumed that challenging art requires deliberate reflexive processing at the level of "aboutness" in order to be experientially pleasing. Here, we probed such a cognitive mastering mechanism, achieved by iterative cycles of elaboration, as predicted by our model of aesthetic experiences. For the study, two kinds of portraits were applied, one associable to a high fluency and one to a high stimulation potential (according to results of an extensive rating study). In Experiment 1, we provided a repeated evaluation task, which revealed a distinctive preference effect for challenging portraits that was absent in the visual exposition conditions of a familiarity and a mere exposure task (Experiment 2). In a follow-up task (Experiment 3) this preference effect was observed with a novel and more encompassing pool of portraits, which corroborated its stability and robustness. In an explorative stimulus-transfer task (Experiment 4), we investigated the presumed underlying mechanism by testing whether the observed effect would generalize onto novel portraits of the same artist-specific styles. Results discounted an alternative interpretation of a perceptual adaptation effect and hinted at meaning-driven mental activity. Conjointly, findings for inexperienced viewers were indicative of an elaboration based mastering mechanism that selectively operated for mentally challenging portraits. Moreover, findings were in line with a dual-process view of human preference formation with art. Theoretical implications and boundary conditions are discussed.

Project description:ImportanceTimely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts.ObjectiveTo investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies.Design, setting, and participantsRandomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system. Primary care physicians were randomized to an immediate activation group and a delayed activation group. Data were analyzed from August to November 2023.ExposureAccess to GenAI-drafted replies for patient messages.Main outcomes and measuresTime spent (1) reading messages, (2) replying to messages, (3) length of replies, and (4) physician likelihood to recommend GenAI drafts. The a priori hypothesis was that GenAI drafts would be associated with less physician time spent reading and replying to messages. A mixed-effects model was used.ResultsFifty-two physicians participated in this QI study, with 25 randomized to the immediate activation group and 27 randomized to the delayed activation group. A contemporary control group included 70 physicians. There were 18 female participants (72.0%) in the immediate group and 17 female participants (63.0%) in the delayed group; the median age range was 35-44 years in the immediate group and 45-54 years in the delayed group. The median (IQR) time spent reading messages in the immediate group was 26 (11-69) seconds at baseline, 31 (15-70) seconds 3 weeks after entry to the intervention, and 31 (14-70) seconds 6 weeks after entry. The delayed group's median (IQR) read time was 25 (10-67) seconds at baseline, 29 (11-77) seconds during the 3-week waiting period, and 32 (15-72) seconds 3 weeks after entry to the intervention. The contemporary control group's median (IQR) read times were 21 (9-54), 22 (9-63), and 23 (9-60) seconds in corresponding periods. The estimated association of GenAI was a 21.8% increase in read time (95% CI, 5.2% to 41.0%; P = .008), a -5.9% change in reply time (95% CI, -16.6% to 6.2%; P = .33), and a 17.9% increase in reply length (95% CI, 10.1% to 26.2%; P < .001). Participants recognized GenAI's value and suggested areas for improvement.Conclusions and relevanceIn this QI study, GenAI-drafted replies were associated with significantly increased read time, no change in reply time, significantly increased reply length, and some perceived benefits. Rigorous empirical tests are necessary to further examine GenAI's performance. Future studies should examine patient experience and compare multiple GenAIs, including those with medical training.

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Who Gets Credit for AI-Generated Art?

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Who Gets Credit for AI-Generated Art?

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