Regulation — federal / defense
DoD AI Ethical Principles — Responsible / Equitable / Traceable / Reliable / Governable
Adopted February 2020, the five DoD AI Ethical Principles are the operating baseline that CDAO, program offices, and DoD IG use to test whether an AI program is defensible. Silent under-flagging in a predictive-maintenance AI fails Reliable + Governable directly — and the operator does not get to choose which principles to be tested against.
What the principles actually say
"DoD AI capabilities will be developed and deployed such that:
Responsible. DoD personnel will exercise appropriate levels of judgment and care, while remaining responsible for the development, deployment, and use of AI capabilities.
Equitable. The Department will take deliberate steps to minimize unintended bias in AI capabilities.
Traceable. The Department's AI capabilities will be developed and deployed such that relevant personnel possess an appropriate understanding of the technology, development processes, and operational methods applicable to AI capabilities, including with transparent and auditable methodologies, data sources, and design procedure and documentation.
Reliable. The Department's AI capabilities will have explicit, well-defined uses, and the safety, security, and effectiveness of such capabilities will be subject to testing and assurance within those defined uses across their entire life-cycles.
Governable. The Department will design and engineer AI capabilities to fulfill their intended functions while possessing the ability to detect and avoid unintended consequences, and the ability to disengage or deactivate deployed systems that demonstrate unintended behavior."
DoD Adoption of AI Ethical Principles, Feb 24 2020 — the five-principle statement
What this means in plain English for predictive-maintenance AI
The two load-bearing principles for the sample scenario are Reliable and Governable:
- Reliable means the AI's effectiveness has to be subject to testing across the full lifecycle. Not just at accreditation. Not just at ATO renewal. Across the lifecycle — which includes the between-audit window where drift silently accumulates.
- Governable means the program office must be able to detect unintended behavior and disengage or deactivate when it appears. Detection is impossible without independent measurement, because the vendor's own dashboard is inside the entity being governed.
Traceable also applies: the reproducibility drill in the Snapshot is the direct implementation of "auditable methodologies, data sources, and design procedure and documentation."
What triggers the exposure in the sample
Silent 45+ day drift + aggregate dashboard green + no independent measurement in place = the Governable principle is failed by construction. The program office cannot detect what its instrumentation is structurally blind to. The Reliable principle is failed the moment the F3 red-lane rate collapse persists across a full quarter of the audit period.
The principles are not scored 0-100. They are yes/no at the diligence level. The moment silent drift is knowable and unmeasured, both flags are set for the affected AI surface.
What the $499 Snapshot shows against these principles
- Reliable: distributional-shape monitoring across the lifecycle, not just at ATO renewal cadence
- Governable: independent detection of unintended behavior + freeze / hold-for-manual-review criteria in the 3 fix-first items
- Traceable: reproducibility drill + decision-hash retention + signed declaration — auditable methodology, data sources, and procedure
- Responsible: named principal signature on the declaration; the responsibility chain is addressable
See how the A/B/C determination maps to the principles →
$499 Snapshot. 3 business days.
Independent-verifier evidence against Reliable + Traceable + Governable on your program's AI surface + 3 fix-first items + signed declaration.
Buy $499
Snapshot credit applies to Baseline ($2,500) or Enterprise Attestation ($35-55K) upgrade within 30 days.