CI-03 Emergency Semantics and Ethical Escalation for Personal AI
Most AI safety research focuses on adversarial attacks, reward misspecification, and value alignment in controlled environments. However, a host-aware agent system that lives alongside a human must also respond appropriately to genuine human crises—medical emergencies, legal threats, natural disasters, family crises, mental-health episodes, and business continuity failures. In these moments, the system's behaviour must shift dramatically: non-essential automations must halt, critical information must be surfaced instantly, the system must know whom to notify and in what order, and privacy must be preserved while enabling life-saving data sharing. This paper introduces Emergency Semantics and Ethical Escalation (ESEE)—a formal framework for crisis response in personal AI systems. The ESEE provides: (1) a taxonomy of six emergency types (medical, legal, disaster, family, mental-health, business continuity) with distinct visibility rules, automation policies, and escalation paths; (2) tiered escalation (AMBER → RED → BLACK) with confidence-based thresholds and timeouts; (3) the Safe Harbor protocol—a cryptographic policy engine for privacy-preserving data release that respects contextual integrity; (4) ethical safeguards including shadow mode deployment, user veto, transparency logs, and post-incident review. We evaluate the ESEE through simulated crisis scenarios (N=100 logs) achieving 88% sensitivity and 4% false positive rate, and a user study (N=40) comparing systems with and without emergency semantics. Results show that users strongly prefer the emergency-aware system (92% would enable the feature vs. 15% for the baseline), rating it significantly higher on appropriateness (4.6 vs. 1.8/5), trust (4.4 vs. 2.2/5), and perceived safety (4.7 vs. 2.0/5). The ESEE fills an underexplored area in AI safety—designing for genuine human crises, not just adversarial attacks—and provides a practical, ethically grounded framework for crisis-ready personal AI. Paper 3 of 8 in Constitutional Identity series David AB Van Der Walt Pietarien (Nexus Sovereign Research Division)
Authors
- David Andries Barnard Van der Walt (ORCID: https://orcid.org/0009-0000-3053-6613)
Institutions
- Umkhuseli Innovation and Research Management (ZA)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-06-24
- DOI
- https://doi.org/10.5281/zenodo.20836759
- Primary Topic
- Adversarial Robustness in Machine Learning
- Type
- preprint