Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems
Apple Machine Learning Research
The paper designs two open-ended probes, using a Wizard of Oz setup, to let people train personalized machine learning systems on phenomena they define in their daily lives. The week-long study found ontological boundaries were negotiated at four sites. This shifts evaluation from usability or technical feasibility toward designing methods that support how users set boundaries around the phenomenon, data, and objectivity.
Why it matters
Designed artifacts are ontological, shaping, and at times limiting, what becomes possible or imaginable. One path toward mitigating such foreclosures is giving people power over how systems are designed and built. Despite decades of scholarship around systems that enable such authorship, these systems are often evaluated on whether or not they are usable, useful, or technically feasible, leaving questions of ontological boundary negotiation, unexamined. We design two open-ended probes that utilize a Wizard of Oz technique to enable the experience of training a personalized machine learning…