RESEARCH · RESEARCH · #1557
Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems
The paper presents two open-ended probes using a Wizard of Oz approach to let participants train personalized machine-learning sensing systems on phenomena they define. In a week-long in-the-wild study, participants' interactions revealed four sites of ontological boundary negotiation—what counts as a phenomenon, the subject as part of relations, signal versus noise, and data objectivity—and the authors propose design directions to support such negotiations.
KEY POINTS
- The paper presents two open-ended probes using a Wizard of Oz approach to let participants train personalized machine-learning sensing systems on phenomena they define.
- In a week-long in-the-wild study, participants' interactions revealed four sites of ontological boundary negotiation—what counts as a phenomenon, the subject as part of relations, signal versus noise, and data objectivity—and the authors propose design directions to support such negotiations.
- It highlights how giving users authorship over ML-based sensing systems reshapes what can be sensed and offers concrete design guidance to support users' negotiation of representational limits.
WHY IT MATTERS
It highlights how giving users authorship over ML-based sensing systems reshapes what can be sensed and offers concrete design guidance to support users' negotiation of representational limits.