与生成式人工智能共同创造艺术:对艺术作品和艺术家的影响

Uwe Messer
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摘要

由于人工智能(AI)的产生,合成视觉艺术正在成为一种商品。使用人工智能进行共同创作的趋势不会放过艺术家的创作过程,因此了解在创作过程的不同阶段使用生成式人工智能如何影响对艺术家的评价以及人机合作的结果(即视觉作品)非常重要。在三项实验(N = 560)中,本研究探讨了艺术作品的评价如何因艺术家在创作过程的不同阶段与人工智能合作的启示而发生变化。结果表明,共同创作的艺术作品较少受到喜爱和认可,尤其是在实施阶段使用人工智能时。虽然共同创作的艺术被认为更新颖,但却缺乏创作的真实性,这一点具有主导性影响。研究结果还表明,艺术家的看法会受到共同创作过程的影响,而共同创作的艺术家会因为被认为不够真实而较少受到欣赏。结果确定了两个边界条件。通过公开艺术家参与人工智能共同创作的程度(例如,在一组精心策划的图像上训练算法,而不是简单地提示现成的人工智能图像生成器),可以减轻负面影响。在艺术被认为具有商业动机(如股票图像)的情况下,效果也会减弱。这项研究对有关人类与人工智能合作的文献、真实性研究以及正在进行的有关算法存在透明度的政策辩论具有重要意义。
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Co-creating art with generative artificial intelligence: Implications for artworks and artists

Synthetic visual art is becoming a commodity due to generative artificial intelligence (AI). The trend of using AI for co-creation will not spare artists’ creative processes, and it is important to understand how the use of generative AI at different stages of the creative process affects both the evaluation of the artist and the result of the human-machine collaboration (i.e., the visual artifact). In three experiments (N = 560), this research explores how the evaluation of artworks is transformed by the revelation that the artist collaborated with AI at different stages of the creative process. The results show that co-created art is less liked and recognized, especially when AI was used in the implementation stage. While co-created art is perceived as more novel, it lacks creative authenticity, which exerts a dominant influence. The results also show that artists’ perceptions suffer from the co-creation process, and that artists who co-create are less admired because they are perceived as less authentic. Two boundary conditions are identified. The negative effect can be mitigated by disclosing the level of artist involvement in co-creation with AI (e.g., by training the algorithm on a curated set of images vs. simply prompting an off-the-shelf AI image generator). In the context of art that is perceived as commercially motivated (e.g., stock images), the effect is also diminished. This research has important implications for the literature on human-AI-collaboration, research on authenticity, and the ongoing policy debate regarding the transparency of algorithmic presence.

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