It is argued that creativity in the context of generative AI is best understood as a property of hybrid collectives, or populations of interacting people and algorithms, rather than of individuals.
Abstract
Generative AI is changing how cultural artifacts are created and circulated, and with it our understanding of creativity itself. Researchers disagree about whether these tools enrich or impoverish culture, and we argue that much of that disagreement comes from conflating two distinct components of creativity: novelty, a property of single artifacts, and diversity, a property of populations. We argue further that creativity in the context of generative AI is best understood as a property of hybrid collectives, or populations of interacting people and algorithms, rather than of individuals. AI-assisted ideation reliably raises the novelty of individual output while narrowing diversity in the aggregate, but this is not an inevitable consequence of putting machines in the loop. Because humans and models search in complementary ways, mixed groups can outperform and out-diversify groups of either kind alone, and machine-discovered solutions can enter human culture and persist there. What decides the outcome is composition: which agents are present, in what proportion, and how they are connected. The question is no longer whether AI helps or harms creativity, but which mixtures let individual gains accumulate without eroding collective diversity.
It is concluded that resistance to attributing creativity to GenAI reflects genuine conceptual distinctions alongside persistent anthropocentric assumptions, and that current deployment practices carry ethical implications that demand an institutional response rather than case-by-case management.
A conceptual analysis of AI-generated music as a sociotechnical practice that reshapes not only ideas of creativity and authorship, but also the material conditions under which human artists work is developed.
It is argued that current generative AI systematically favors combinatorial creativity while offering weaker support for transformational creativity, and that HCI should evaluate AI tools not only by the quality of creative outputs they enable but by the distribution of creative types they support, privilege, or suppre...
Peter Dalsgaard· Proceedings of the 14th Nord...· 0 citations
Generative artificial intelligence (AI) has raised a widely shared concern that human cognitive and creative output is drifting toward common templates. The terms used to describe this drift, including algorithmic monoculture, outcome homogenization, and the loss of collective diversity, all characterize systems, popul...
Qi-Tian Cao, Zi-Ran Zhou, Lin Wang et al.· Frontiers in Psychology· 0 citations
How the concept of the death of the author in a metaphorical sense discussed by Barthes is currently being turned into a technological one is examined and analyzed to address the gap regarding what kind of thing AI-generated artworks are or should be referred to.
I. Aleksandruk, O. Palchevska, Alina Androshchuck· Dianoesis· 0 citations
Creativity researchers often distinguish between two stages of the creative process: generation versus selection. While much is known about the psychology of idea generation (e.g., the factors that lead to a greater number of novel and useful ideas), less is understood about the nature of selection, or how generation...
Jin Kim, George E. Newman· Scientific Reports· 0 citations
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