Clauses 8 + 8.1 + 8.2 establish ethical risk management and Clause 10 establishes validation of ethical outcomes. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature (full IEEE text NOT reproduced): ethical risk identification covering: harm to individuals + groups + communities + environment + future generations; ethical risk analysis (likelihood + severity + reversibility + scope + remediation availability); ethical risk treatment: avoid + mitigate + transfer + accept with informed consent + monitor + redesign; integration with ISO 31000 risk management + ISO/IEC 23894 AI risk management. AI risk identification and assessment: training data risks + algorithm risks + deployment risks + operational risks + monitoring risks; AI system categorization by risk level (EU AI Act prohibited + high-risk + limited risk + minimal risk; NIST AI RMF mapping); risk-tiered controls. Validation of Ethical Outcomes: real-world testing + post-deployment monitoring + user feedback + impact assessment + ethical KPI; comparison against initial values + requirements + acceptance criteria; longitudinal evaluation; pilot + canary + phased rollout. AI safety + robustness + resilience: adversarial attack protection (FGSM + PGD + Carlini-Wagner) + adversarial training + defensive distillation + ensemble methods + uncertainty quantification + out-of-distribution detection + concept drift handling + model integrity (signed weights + secure boot + tamper detection); safe AI deployment procedures (blue-green + canary + feature flags + emergency kill-switch + circuit breakers). Coordinates with ISO 31000 + ISO/IEC 23894 + EU AI Act Article 9 Risk Management System + NIST AI RMF GOVERN + MAP + MEASURE + MANAGE + UK AI Safety Institute + Singapore AI Verify + adversarial ML research + responsible AI red teaming + frontier AI safety. IEEE 7000 Ethical Risk + Validation applies.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.