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)

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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
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CI-03 Emergency Semantics and Ethical Escalation for Personal AI

David Andries Barnard Van der Walt
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
preprint

CI-03 Emergency Semantics and Ethical Escalation for Personal AI

David Andries Barnard Van der Walt
preprint en

Abstract

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)

Zenodo (CERN European Organization for Nuclear Research)
Umkhuseli Innovation and Research Management (ZA)
Climate action
Adversarial Robustness in Machine Learning
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