We invite applications for a fully-funded 4-year doctoral position situated at the cluster 'Designing with Intelligence' on exploring, through designerly research and practice, the relevance of uncertainty for current and/or emerging design aesthetics of intelligent adaptive systems.
MotivationWhen considering systems of any type, AI technologies now form core components of their architectures. ChatGPT, Stable Diffusion or Midjourney are obvious examples, but systems of finance, navigation, weather prediction, entertainment and the infrastructures (e.g., cloud services, search engines) underpinning and interconnecting them are equally reliant on AI technologies. In turn, design research and practice are asked to respond and develop new ways of designing with and for the use of such systems, which through AI technologies exhibit adaptive capacities usually associated with intelligence. Yet, design seems to be lagging behind: AI technologies are generally interfaced with via conventional forms (e.g., chatbots) that have little to do with the manifold opportunities that AI technologies present as a design material.
With so much happening in the inner workings—from data processing to probabilistic predictions—of AI technologies and the systems they are embedded in, design researchers and practitioners are still called upon to more deeply explore the aesthetic potential of AI technologies and their components towards evocative
1 new possibilities. The technical attribute of uncertainty (i.e., data noise and model variance
2) has been proposed as one way to consider AI technologies as a specific design material
3 and first conceptual vocabularies
4 and designerly explorations
5 have been developed. This work has suggested that designing with uncertainty opens opportunities for evocative speculative, playful and explorative design in a way that reflects the specifics of AI technologies and the material infrastructures they are embedded in.
Research AvenuesThis PhD will further investigate the relevance of uncertainty for design, specifically towards a foundational understanding of the aesthetics of intelligent adaptive systems. Importantly, in this dissertation foundations are expected to take the shape of readymade resources which can be conceptual and/or practical, but must be relevant for design practice—such as patterns, taxonomies, typologies and/or catalogues. Particularly noteworthy are patterns in the original sense as components of an aesthetic, practical and ethical framework
6 seeking patterns for uncertainty and related concerns prompts methodological questions around how they could be derived, on what basis, on what level of abstraction and what for. To conceptualize and make actionable such foundational resources, the candidate is expected to conduct design research
7 into the technical aspects of uncertainty as related to AI technologies as well as its broader relevance to and related attributes of computational systems (e.g., precision, recall, entropy, loss).
At the departmentTo facilitate this, PhD researcher will develop close ties to individual researchers, PhD students, projects, courses, and clusters at TU/e to source and conduct case studies for analysis as well as opportunities for practice-led experimentation and knowledge exchange. Relevant fields that the PhD researcher may find here include explainable AI, more-than-human design, sustainability and repair, sound, material innovation, and autonomous driving. Further, the Industrial Design department and 'Designing with Intelligence' cluster will provide various opportunities to participate in research, share project progress and contribute to the research culture. Similarly, the PhD researcher can expect contributing to and leading on publications for leading venues such as ACM CHI, DIS, ToCHI or IUI.
1. Ghajargar, Maliheh, and Jeffrey Bardzell. 2021. 'Synthesis of Forms: Integrating Practical and Reflective Qualities in Design'. In
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1-12. CHI '21. New York, NY, USA: Association for Computing Machinery.
https://doi.org/10.1145/3411764.3445232.
2. Fox, Craig R., and Gülden Ülkümen. 2011. 'Distinguishing Two Dimensions of Uncertainty'. SSRN Electronic Journal.
https://doi.org/10.2139/ssrn.3695311.
3. Benjamin, Jesse Josua, Arne Berger, Nick Merrill, and James Pierce. 2021. 'Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry'. In
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1-14. CHI '21. New York, NY, USA: Association for Computing Machinery.
https://doi.org/10.1145/3411764.3445481.
4. Benjamin, Jesse Josua, Heidi Biggs, Arne Berger, Julija Rukanskaitė, Michael B. Heidt, Nick Merrill, James Pierce, and Joseph Lindley. 2023. 'The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologies'. In
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1-19. CHI '23. New York, NY, USA: Association for Computing Machinery.
https://doi.org/10.1145/3544548.3581175.
5. Sivertsen, Christian, and Anders Sundnes Løvlie. 2024. 'Exploring Aesthetic Qualities of Deep Generative Models through Technological (Art) Mediation'. In
Proceedings of the 2024 ACM Designing Interactive Systems Conference, 2738-52. DIS '24. New York, NY, USA: Association for Computing Machinery.
https://doi.org/10.1145/3643834.3661498.
6. Alexander, Christopher, Sara Ishikawa, and Murray Silverstein. 1977.
A Pattern Language: Towns, Buildings, Construction. Oxford University Press.
7. Koskinen, Ilpo K., J. Zimmerman, T. Binder, J. Redström, and S.A.G. Wensveen. 2011.
Design Research through Practice: From the Lab, Field, and Showroom. Waltham: Morgan Kaufmann Publishers, Inc.
https://doi.org/10.1016/B978-0-12-385502-2.00015-8.