Protect Summer Protocol

Protect Summer: An AI-Driven Safety Protocol for Human Wellbeing Introduction Due to the rise of autonomous weapon systems (AWS), artificial intelligence is being used to kill people before it is being meaningfully deployed to protect them. Military programs involving AWS—such as the U.S. Department of Defense’s Project Maven and autonomous drone systems in global conflict zones—demonstrate the accelerating integration of AI into combat. These developments have led to widespread concern from civil society groups and researchers, notably the international coalition behind the “Stop Killer Robots” campaign, which advocates for a preemptive ban on fully autonomous weapons. In this context, this project proposes a civilian, peace-oriented alternative: an AI-powered personal safety and wellbeing protocol named Protect Summer, developed as a case study through a test subject named Summer, who is also the lead researcher. The aim is to build a context-aware, AI-monitored system capable of assessing a person’s health data and surrounding circumstances—such as potential distress or disappearance—and initiating automated alerts to designated contacts. This project aims to merge real-time data analysis, ethical AI design, and human validation signals into a modular framework. It will act as both a prototype for personal protective AI systems and a research instrument for improving machine situational awareness, especially when applied to edge cases and real-world ambiguity. By scaffolding this initiative as a formal research paper, the project invites critical examination, academic collaboration, and future extensibility across domains such as elder care, mental health, human rights, and humanitarian response.

Background and Motivation Personal safety technologies have proliferated in recent years, including GPS tracking apps, panic buttons, and wearable emergency systems. However, these systems are generally reactive and isolated from broader context. Meanwhile, AI has advanced significantly in domains such as image recognition, behavioral prediction, and health monitoring. Yet there remains a critical gap: the proactive, ethical use of AI to protect human life through real-time contextual awareness. This project’s motivation is threefold: Protection-first AI deployment: Redressing the imbalance of AI use in military versus humanitarian contexts.

Ambient safety net for at-risk individuals: Providing a scalable method for augmenting the daily safety of individuals through unobtrusive, opt-in monitoring.

Modeling reality integrity: Probing whether AI can become more effective at detecting deception, danger, and real-world events.

Methodology The system will operate on a modular pipeline consisting of: Data Collection

Passive health data from Apple Health app (e.g. movement, sleep, activity)

Device-based location data from iPhone GPS

Optional: CCTV or webcam feed from fixed home camera

Event Trigger Layer

Custom thresholds (e.g. no movement for 8+ hours, device left home without user)

Escalation protocol initiation based on detected anomalies

AI Monitoring Agent

Natural language evaluation of time-series anomalies

Cross-referencing environment state (CCTV, timestamps) with known habits

Human-likeness signal assessment for spoof detection

Alert Dispatch Mechanism

Automated outreach to designated trusted contacts via email or SMS

Generation of AI-summarized incident report

Failsafe Logging

Local read-only logs (e.g. timestamped clips or data snapshots)

Immutable audit chain to detect post-event data manipulation

This methodology will be iteratively tested in a sandboxed MVP deployment on Summer’s personal hardware.

Technical Architecture Input Sources:

Apple HealthKit (via iOS device or ResearchKit)

Optional: Apple Watch (for heart rate, motion, fall detection)

Home webcam (initially USB or IP-based camera, e.g. Wyze or Reolink)

Processing & Reasoning Layer:

AI agent (ChatGPT or Claude API)

Trigger logic executed via Zapier or n8n

Local processing (laptop or Raspberry Pi)

Alert and Logging Services:

Twilio for SMS

Gmail API for email alerts

Google Drive or Notion for log storage

Optional Integrations:

Surveillance APIs (city CCTV, if accessible in future phases)

Voice interface (Mycroft AI or open source assistant)

Ethical Considerations This system is explicitly opt-in and designed to protect autonomy. Key principles include: Transparency: All data collection and use is visible to the subject.

Non-weaponization: The system cannot initiate force or interfere with others.

Human override: Alerts are suggestions, not enforcement.

Consent-first: All alerts are predicated on pre-configured user consent.

The project aligns with the values promoted by “Stop Killer Robots” and seeks to offer a protective mirror image of what autonomous systems can be when designed ethically.

Edge Cases & Future Integrity Challenges As situational AI expands, edge cases challenge model robustness. One example is simulation vs. reality ambiguity: An intruder appears to attack Summer, but the camera feed is replaced by a simulated suicide scenario (deepfake injection).

This raises urgent questions: Can timestamped video be trusted if modifiable (e.g. Apple allows users to alter video timestamps)?

How do we protect against reality spoofing?

Proposed initial defenses: Immutable file storage of video snippets (read-only log chaining)

Cross-validation of sensor input and visual data

AI-assisted anomaly detection (e.g. detecting visual compression artifacts or human behavior inconsistencies)

These issues lay the groundwork for a future field of AI-for-reality-validation.

Findings (To Be Populated Post-Implementation) This section will summarize observations, anomalies, performance metrics, and any false positive/negative rates recorded during the MVP testing of Protect Summer. Areas to include: Alert frequency and appropriateness

Health trigger accuracy

AI summary reliability

Usability and UX notes

Recommendations for further tuning

Additional Materials (For Expansion in Publication or GitHub Repository) Edge case repository

Visual layout of alert chains

Sample alert messages

API and integration checklists

Alternate deployment modes (elder care, solo travel, neurodiverse support)

Future Smart City integration concept notes

Reality spoofing defense mechanisms

Commentary on timestamp falsification vulnerabilities (e.g. iOS screenshot example)