“Beyond Data-Driven Aesthetics,” by MIT architecture graduate and researcher Alexandros Haridis, on view at MIT Keller Gallery through June 30, examines 20th and 21st century efforts to transform computing into a medium for creative production and aesthetic judgment in architecture and the applied arts. The exhibition uses philosophy, mathematics, computer science, and design computation to translate algorithms, theories, and machine learning systems into physical installations and interactive visualizations.
question: What inspired “Beyond Data-Driven Aesthetics” and what questions does it explore?
answer: The conceptual origins of Beyond Data-Driven Aesthetics arose from three intersecting lines of research.
First, while completing my PhD in Design and Computing at MIT’s School of Architecture around 2022, I observed in real time how advances in data-driven machine learning (systems like ChatGPT and Stable Diffusion) were rapidly entering public discourse in areas such as creativity, aesthetic judgment, design, and even high-profile art auctions.
At the same time, my own research already focuses on aesthetic judgment and evaluation, and it has become increasingly clear that many of the issues that are publicly presented as “new” regarding AI actually have a much longer history throughout the 20th century. For example, the 1956 Dartmouth Summer Research Project, a foundational event in the field of AI, identified the creation and evaluation process as one of seven key aspects of human intelligence that future AI research should address.
Second, this exhibition was influenced by research in design computation and shape grammar, which explores the relationship between human insight and computation through rule-based methods rather than purely data-driven learning. Of particular importance to me are recent interpretive studies of aesthetic theory based on figures such as Samuel Taylor Coleridge, Oscar Wilde, and even John von Neumann. These studies examine whether theories of aesthetic value and comparison articulated in philosophical and literary texts have the potential to illuminate the possibilities and limitations of contemporary models of digital computing and AI in architecture and design.
Finally, the exhibition was motivated by the use of design, manufacturing, and data visualization as methods for interpreting mathematical concepts, algorithms, and “black box” machine learning systems. From visualizing neural networks in computer science to software reconstruction and digital fabrication in architecture and curatorial work, researchers across disciplines are increasingly using reconstruction and visualization techniques to make computational systems more tangible and interpretable.
question: How do you incorporate your research into computing and aesthetics into your exhibition?
answer: The exhibition’s approach is to ask precisely what captures the most salient ideas in a particular research paper or book, and to use design to interpret those ideas in visual, spatial and experiential form. Utilizing design techniques such as software reconstruction, physical creation, and data visualization, this exhibition takes textual sources dense with algorithmic ideas, abstract concepts, and mathematical formulas and transforms them into stories in space that include interaction, material form, and digital visualization.
The exhibition itself is structured around five thematic areas: aesthetic scales, aesthetic guidelines, algorithmic aesthetics, aesthetic appropriation, and aesthetic novelty. Each theme serves as a selective “window” into a distinct computational approach to aesthetic judgment derived from a particular publication (book or research paper). These theme titles are based on the central concept of each publication. For example, “measurement” refers to mathematician George Berkoff’s work in the 1930s to quantify aesthetic value mathematically, and “novelty” examines how the machine learning system AICAN judges generated images according to theories of cognitive aesthetics that balance familiarity and deviation from known art styles.
Across all five cases, the key insight is that design itself can function as a method of interpretive translation. It is a way of visually, concretely, and experientially conveying what traditional academic research in technical fields has typically conveyed only through words and language-like expressions, such as scientific diagrams and tables.
question: What question do you want to investigate next?
answer: Beyond Data-Driven Aesthetics is conceived as a research exhibition and an ongoing platform for investigating how computational systems are involved in processes of aesthetic judgment, production, and transformation across architecture and the applied arts.
One of the central questions of this exhibition is one that architecture, design, and engineering researchers are increasingly focusing on: computational evaluation that goes beyond pure performance or functional requirements. This applies to a variety of design spaces, including buildings, structural forms, and everyday objects. The exhibition’s case studies suggest that many of these questions long predate the current interest in computing and AI, and have been approached through a variety of computational and theoretical evaluation models since at least the early 20th century.
At the same time, there is growing interest in how these ideas can be applied to broader applications related to the built environment. In particular, I’m interested in how research related to “Beyond data-driven aesthetics” can help designers and engineers better understand how computation, whether rule-based or data-driven, can inform us about what positively contributes to the human experience in terms of the spaces and objects that people inhabit and use.
Finally, a direction I continue to explore is the methodological role of design itself as an interpretive device. Through software restructuring, visualization, and physical creation, this exhibition uses design to transform opaque computational systems into more legible, tangible, and experiential artifacts. More broadly, this raises questions not only about the mechanization of “beauty” and “taste” (the traditional preoccupations of aesthetic formalism in the 20th century), but also about how traditional forms of research scholarship and communication evolve through spatial, visual, and public forms.
