Visualizing Machine Learning Uncertainty Part of Project IKON From qualitative research I conducted, we concluded that our use case called for an ML-driven visualization, as this could show stakeholders potential thematic overlaps between research projects and knowledge transfer activities. At the same time as we concluded that an ML-driven visualization was needed, we predicted that […]
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Sense-Making of Machine Learning by Non-ML experts Part of Project IKON To understand how stakeholders would make sense of the proposed prototype, we developed a method for conducting co-design workshops with actual ML technologies. Designing bespoke transparencies that represented possible explanations (see left), participants engaged in a series of open tasks. We studied the patterns […]
Machine Learning Uncertainty as a Design Material Part of Post-Phenomenological AI Studies and Entoptic Media In this project, I conducted post-phenomenological analyses of four projects from different methodological strands of design research in order to discern how ML technologies may be seen as a design material. Due to the probabilistic techniques employed, I specifically centered […]
Entoptic Field Camera Part of Post-Phenomenological AI Studies and Entoptic Media Inspired by the appearance of skies in photos of the 2020 California wildfires (i.e., deep reds appeared blueish-grayish), I have prototyped a GAN-driven camera web application which makes this ‘reality-autocorrect’ explicit. Photos taken by a user are sent to an API for ‘development,’ and […]