A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
From Product to Producer: The Impact of Perceptual Evidence and Robot Embodiment on the Human Assessment of AI Creativity
Tekijät: Pennanen, Niki; Linkola, Simo; Kantosalo, Anna; Hiillos, Nicolas; Männistö, Tomi; Guckelsberger, Christian
Kustantaja: Association for Computing Machinery (ACM)
Kustannuspaikka: NEW YORK
Julkaisuvuosi: 2025
Journal: ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION
Tietokannassa oleva lehden nimi: ACM Transactions on Human-Robot Interaction
Lehden akronyymi: ACM T HUM-ROBOT INTE
Artikkelin numero: 41
Vuosikerta: 14
Numero: 3
Sivujen määrä: 583
eISSN: 2573-9522
DOI: https://doi.org/10.1145/3711939
Verkko-osoite: https://doi.org/10.1145/3711939
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/499387890
While creative artificial intelligence (AI) is becoming integral to our lives, we know little about what makes us call AI "creative". Informed by prior theoretical and empirical work, we investigate how perceiving evidence of a creative act beyond the final product affects our assessment of robot creativity. We study embodiment morphology as a potential moderator of this relationship, informing a 3 x 2 factorial design. In two lab experiments on visual art, participants (N = 30 + 60) assessed drawings produced by two physical robots with different morphologies, under exposure to product, process and producer as three levels of perceptual evidence. The data supports that the human assessment of robot creativity is significantly higher the more is revealed beyond the product about the creation process, and eventually the producer. We find no significant effects of embodiment morphology, contrasting existing hypotheses and offering a more detailed understanding for future work. The latter is also informed by additional exploratory analyses revealing factors potentially influencing creativity assessments, including perceived robot likeability and participants' experience with robotics and AI. Our insights empirically ground existing design patterns, foster fairness and validity in system comparisons, and contribute to a deeper understanding of our relationship with creative AI and thus its adoption in society.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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This work was financially supported by the Academy of Finland (#328729, CACDAR) and the Helsinki Institute for Information Technology.
