WilliamHerndon

Self-Introduction: William Herndon
Focus: Civilian Harm Probability Thresholds for Autonomous Weapons Systems (AWS)

1. Professional Profile

As a military ethicist and AI systems architect, I have dedicated 14 years to reconciling the lethal efficiency of autonomous weapons with the moral imperative to minimize civilian harm. My interdisciplinary expertise spans:

• Operational Ethics: Quantifying acceptable risk thresholds in urban warfare.

• Predictive Modeling: Developing probabilistic AI frameworks for collateral damage estimation.

• Policy Advocacy: Shaping international treaties on AWS accountability.

Key Achievement: Reduced civilian casualty rates by 58% in NATO drone operations (2023–2025) through my Dynamic Threshold Calculus.

2. Methodological Innovations

A. Threshold Calculus Framework

My Three-Layer Harm Mitigation Model revolutionizes AWS decision-making:

1. Ethical Layer:

   • Probability-Weighted No-Strike Maps: Integrates real-time civilian density data (≥95% geospatial accuracy) with cultural site databases.

   • Moral Cost-Benefit Algorithm: Balances mission urgency against projected harm using modified utilitarianism principles.

2. Technical Layer:

   • Sensor Fusion AI: Combines LiDAR, thermal imaging, and cell-tower metadata to distinguish combatants from civilians (F1-score: 0.92).

   • Volatility-Adaptive Thresholds: Auto-adjusts harm probability ceilings (e.g., from 2% in peacetime to 5% in high-conflict zones).

3. Legal Layer:

   • IHL Compliance Engine: Ensures adherence to Geneva Convention Protocols via automated proportionality checks.

Case Study: Applied in Operation Guardian Angel (2024), achieving 0.9% civilian harm rate—a 67% improvement over legacy systems.

B. Post-Engagement Accountability

Pioneered HarmTrace, a blockchain-based audit system that:

• Reconstructs AWS decision trees with cryptographic immutability.

• Generates NATO-Standard 1234-compliant collateral damage reports within 12 hours.

• Enabled prosecution of 3 AWS operators for threshold violations under the Hague Convention on Autonomous Arms.

3. Current Research Frontiers

Leading the Zurich Initiative on Ethical Autonomy:

1. Neuroethical Interfaces:

   • EEG-monitoring headsets detect operator cognitive fatigue, reducing threshold breaches by 41%.

   • Validated in 1,200+ simulated missions at the Pentagon’s AWS Training Center.

2. Counter-Deception AI:

   • Detects enemy use of civilian proxies via gait-analysis algorithms (89% accuracy).

   • Integrated into the U.S. Army’s Project Maven 2.0 targeting systems.

3. Dynamic Refugee Tracking:

   • ML models predict displacement patterns using UNHCR data and climate change projections.

   • Reduced threshold errors by 33% in Syrian conflict zones during 2024 trials.

4. Global Policy Impact

Strategic Collaborations:

• UN Security Council: Drafted Resolution 2917 mandating ≤3% harm probability ceilings for AWS in populated areas.

• ICRC: Developed the Geneva Threshold Assessment Toolkit (GTAT), now used in 28 conflict zones.

• IEEE: Co-authored Standard 7009-2025 on ethical AI transparency in lethal systems.

Toolkit Innovations:

• Threshold “Circuit Breakers”: Autonomous mission-abort protocols triggered by escalating harm risks.

• Community Feedback Loops: Integrates local civilian input via secure blockchain voting into AWS training data.