## Introduction How can we evaluate AI from an [[aesthetic point]] of view? In what follows, we suggest that function-based approaches such as [[Functional Beauty]] and Jane Forsey’s Kantian approach struggle with generative AI. We argue that adapting Carlson’s [[environmental aesthetics]] provides a [[better account]] of [[the appreciation]] of both generative AI systems and the content they produce. Carlson’s perspective—particularly his emphasis on understanding an environment in light of scientific or common-sense knowledge—can illuminate our appreciation of AI systems. By viewing these systems as environments shaped by cultural and technical forces, we can place their outputs and internal processes within a broader context that enhances [[aesthetic appreciation]]. Finally, we demonstrate how this environmental view allows for three different ways of appreciating generative AI. ## 1. Function-Based Theories - need something here as to what exactly it is we are trying to do. Maybe we can start with AI generated images/music/video and say we are going to arrive their by walking backwards. [[Functional Beauty]] (Parsons and Carlson 2008) holds that an object’s [[aesthetic value]] stems from how well its form fulfills a recognisable function. Philosophers who adopt [[this view]] argue that perceptible features are best understood through their intended purpose. Jane Forsey’s Kantian approach (2013) similarly maintains that aesthetic judgment depends on how effectively an artifact’s form meets its aims. On both accounts, assessing an object aesthetically depends on understanding what it is meant to do. ## 2. Appreciating Generative Environments A [[positive account]] of appreciating generative AI can be found by drawing on Carlson’s [[environmental aesthetics]], in which appreciators focus “on [[the order]] imposed [on] objects by the various forces, random and otherwise, that produce them” (p. 119). Carlson outlines a “[[natural environmental model]]” of appreciation, arguing that we must consider environments “as what they in [[fact are]],” and that we should do so “in light of knowledge provided by the natural sciences, especially the environmental sciences such as geology, biology, and ecology.” We suggest that modern AI systems can be considered as a similar type of [[generative environment]], and that we can therefore understand the aesthetic appreciation of AI in similar terms as Carlson does the natural environment.  The natural world is shaped by various forces (e.g. wind, erosion, water currents) which transform the physical environment. Similarly, generative AI systems can be understood as environments shaped by a different set of forces (e.g. training data, alignment training, algorithmic constraints). When users provide prompts to these systems, the output is the result of interactions between such forces. As with natural environments, we might think that understanding such systems underlies their aesthetic appreciation. Like a forest or coral reef, an AI system contains multiple elements that interact in ways that can yield unexpected complexity and variation. For instance, training data may constrain certain outcomes, while user prompts guide directions of generation, and internal feedback loops adjust model parameters over time. These processes recall the layered interactions seen in natural environments. A possible objection here is that everyday users of such systems lack technical knowledge of how they function, meaning that aesthetic appreciation of such systems is out of the reach of the majority. AI architectures and optimisation techniques may remain obscure, and it is not always clear how inputs and underlying parameters influence outputs. We suggest a response in Carlson’s remarks, which show that scientific and everyday knowledge are best viewed on a continuum. He writes: “our scientific knowledge of the natural world [is] only a finer-grained and theoretically richer version of our common, everyday knowledge of it, and not as something essentially different in kind.” On this view, a non-expert can acquire meaningful familiarity with AI just as a gardener, without formal training, understands sunlight, moisture, and soil conditions. Through iterative interactions, AI users identify patterns in outputs and anticipate how prompts affect results, paralleling the gardener’s observation of plant responses (reference withheld for blind review). Although it differs from scientific knowledge, this practical knowledge allows users to apprehend the AI’s underlying structure. By placing everyday and scientific perspectives on the same continuum, it becomes possible for non-specialists to cultivate an informed aesthetic engagement with AI as a generative environment. ## 3. Approaches to Appreciating Generative AI Building on Carlson’s environmental perspective, we can outline three overlapping ways of appreciating generative AI. First, there is theoretical appreciation. Those with a technical understanding of how generative AI works can examine the roles played by training data, algorithmic constraints, and internal feedback processes. In much the same way that scientific knowledge of geology or ecology can contribute to aesthetic appreciation of nature by revealing the structure of the natural environment, technical understanding of architectures or optimisation methods can contribute to aesthetic appreciation of AI by illuminating how an AI system’s outputs arise.  Second, there is practical appreciation, based on understanding through engaging with the generative environment. A skilled gardener may lack scientific knowledge of the environment she is in, but, through working with, and on, the environment, she develops an understanding of how such a system (its forces and objects) works. Similarly, through engaging with a generative AI, a user develops a non-technical understanding of (part of) the underlying workings of the system. In both cases, knowledge of how a generative environment’s forces function is arrived at through direct engagement, and this knowledge underlies aesthetic appreciation. Third, there is output appreciation, in which the generative environment is appreciated through the appreciation of its products. In gardens, plants reflect natural generative processes shaped by gardeners. Similarly, generative AI outputs (text, images) are shaped by data and user input. By considering these products in context, we see how both natural and computational environments generate observable outcomes that can be appreciated not only as such, but especially as expressions of the aesthetic potential of the generative AI system. ### References Carlson, A. (2005). Aesthetics and the environment: The appreciation of nature, art and architecture. Routledge. Forsey, J. (2013). The aesthetics of design. OUP USA. Parsons, G., & Carlson, A. (2008). Functional beauty. OUP Oxford. **