{"slug": "negotiating-ontological-boundaries-in-user-authored-personal-sensing-systems", "title": "Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems", "summary": "Apple researchers published a paper in October 2026 titled \"Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems,\" reporting a week-long exploratory study in which participants used one of two Wizard of Oz probes to train a personalized machine learning system on phenomena they defined themselves. The authors — Nava Haghighi, Danielle Olson, Halden Lin, Erdrin Azemi, Gierad Laput, Kayur Patel and James Landay — identified four sites where ontological boundaries were negotiated: the boundaries of a phenomenon, the subject as part of relations, what is signal and what is noise, and the objectivity of data. The paper argues that user-authoring systems are typically evaluated only on usability, usefulness or technical feasibility, and offers design starting points for supporting boundary negotiation.", "body_md": "[content type paper](https://machinelearning.apple.com/research/)published October 2026\n\nNegotiating Ontological Boundaries in User-Authored Personal Sensing Systems\n\nAuthorsNava Haghighi†**, Danielle Olson, Halden Lin**, Erdrin Azemi, Gierad Laput, Kayur Patel**, James Landay†\n\nDesigned 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 system on phenomena people define themselves. In a week-long exploratory study, participants use one of two probes in the course of their everyday lives. We identify four sites where ontological boundaries were negotiated; the boundaries of a phenomena, the subject as part of relations, what is signal and what is noise, and the objectivity of data. We offer starting points for supporting boundary negotiation through design and discuss open-ended probes as a method for ontological design.\n\nBehavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies\n\nJuly 10, 2026[research area Methods and Algorithms](https://machinelearning.apple.com/research/?domain=Methods%20and%20Algorithms), [research area Privacy](https://machinelearning.apple.com/research/?domain=Privacy)[conference International Conference on Availability, Reliability and Security (ARES)](</research/?event=International%20Conference%20on%20Availability%2C%20Reliability%20and%20Security%20(ARES)>)\n\nThis paper was accepted at the AI4TCI (Workshop on AI for Secure and Trustworthy Critical Infrastructure Systems) Workshop at the International Conference on Availability, Reliability and Security (ARES) 2026.\n\nAutonomous negotiation agents are increasingly deployed in high-stakes settings such as insurance and procurement. While cryptographic techniques protect explicitly disclosed constraint values, they fail to address a subtler threat:…\n\nODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs\n\nOctober 27, 2025[research area Knowledge Bases and Search](https://machinelearning.apple.com/research/?domain=Knowledge%20Bases%20and%20Search), [research area Speech and Natural Language Processing](https://machinelearning.apple.com/research/?domain=Speech%20and%20Natural%20Language%20Processing)\n\nKnowledge graphs (KGs) are foundational to many AI applications, but maintaining their freshness and completeness remains costly. We present ODKE+, a production-grade system that automatically extracts and ingests millions of open-domain facts from web sources with high precision. ODKE+ combines modular components into a scalable pipeline: (1) the Extraction Initiator detects missing or stale facts, (2) the Evidence Retriever collects supporting…", "url": "https://wpnews.pro/news/negotiating-ontological-boundaries-in-user-authored-personal-sensing-systems", "canonical_source": "https://machinelearning.apple.com/research/ontological-boundary-negotiation", "published_at": "2026-10-05 00:00:00+00:00", "updated_at": "2026-10-05 16:49:56.095066+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "ai-ethics"], "entities": ["Apple", "Nava Haghighi", "Danielle Olson", "Halden Lin", "Erdrin Azemi", "Gierad Laput", "Kayur Patel", "James Landay"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/negotiating-ontological-boundaries-in-user-authored-personal-sensing-systems", "markdown": "https://wpnews.pro/news/negotiating-ontological-boundaries-in-user-authored-personal-sensing-systems.md", "text": "https://wpnews.pro/news/negotiating-ontological-boundaries-in-user-authored-personal-sensing-systems.txt", "jsonld": "https://wpnews.pro/news/negotiating-ontological-boundaries-in-user-authored-personal-sensing-systems.jsonld"}}