Researchers from Stanford University and Apple Inc. have published work exploring how users can negotiate ontological boundaries within personalized sensing systems. Their research, presented at the International Conference on Availability, Reliability and Security (ARES), identifies four key areas where users interact with these boundaries: defining phenomena, subject-object relationships, signal versus noise, and data objectivity. The study utilized a Wizard of Oz technique to allow participants to train personalized machine learning systems based on their own definitions, offering design insights for future systems. AI
IMPACT Enhances user control and privacy in personalized AI systems, potentially influencing future human-AI interaction design.
RANK_REASON The cluster contains academic papers detailing research into user-authored personal sensing systems and agent negotiation policies. [lever_c_demoted from research: ic=1 ai=1.0]
Read on Apple Machine Learning Research →
- Apple Inc.
- Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies
- Danielle Olson
- Erdrin Azemi
- Gierad Laput
- Halden Lin
- International Conference on Availability, Reliability and Security (ARES)
- James Landay
- Kayur Patel
- Nava Haghighi
- ODKE+
- Stanford University
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