A Study on the Aesthetic Challenges and Innovative Pathways of Artificial Intelligence Art
Abstract
This article examines how contemporary AI systems—especially diffusion-based generators trained on large image and text corpora—are reshaping authorship, originality, and aesthetic judgment in artistic practice. Rather than treating AI as an autonomous “creator,” we analyze two sites where human agency remains decisive: the provenance of training data and the human-in-the-loop stages of prompting, iterative selection, and post-editing. These mechanisms explain why AI outputs blur, but do not erase, the line between maker and tool. We argue that questions of creativity and credit arise not from machine “subjectivity,” but from distributed decision-making across datasets, model priors, and curatorial choices. Building on this view, the paper proposes a working notion of co-authorship: authorship accrues to those who shape objectives, constrain the generative space, and accept accountability for publication and display. This stance also reframes aesthetic value: judgments of beauty or significance depend on how models are steered, contextualized, and read by audiences, not only on algorithmic novelty. The conclusion outlines a practical human–AI collaboration paradigm—instrumental rather than agentive—that preserves intention, emotion, and imagination while using AI to extend repertoires of form and process. We delineate the scope and limits of this approach and indicate where empirical evaluation would be most informative.
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