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Abstract
In this paper, we take on the question of the potential risks of AI in aesthetics: What kind of risks does AI pose? To engage with this question, we focus on how generative AI models’ results consist of decontextualized content that does not always correspond with its historical context. From this standpoint, bias and misinterpretation pose major challenges in academic aesthetics because AI can be used both as a tool to make research more specific and precise and also for quick results that avoid in-depth research and on the surface may appear decisive and accurate. We set out to expose the limitations and misinterpretations of generative models, and to provide ways to critique the models’ shortcomings, using three case studies of specific word searches of Supreme Court cases since its formation in 1789. Each case study includes visualizations based on prompts written by AI models using word results. The results provide a space for critical reflection on the limitations of emerging technologies that make possible new ways to interpret past events based on parameters that are specific to the time of research. The paper concludes with a broader reflection on how academic aesthetics is redefined by generative models, and in turn how academic aesthetics plays a pivotal role in the ongoing development and assessment of artificial intelligence, as AI becomes incorporated into every aspect of cultures and societies.