U.S. Department of Defense Issues Responsible AI Strategy — June 2, 2022
The DoD’s June 2022 Responsible AI Strategy details six lines of effort to operationalise AI ethical principles, requiring program offices and contractors to embed governance, testing, training, and acquisition controls across the AI lifecycle.
Executive briefing: The US Department of Defense (DoD) released its Responsible Artificial Intelligence (RAI) Strategy and Implementation Pathway on 2 June 2022. The document operationalises the DoD AI Ethical Principles adopted in 2020—responsible, equitable, traceable, reliable, and governable—by outlining six lines of effort across governance, warfighter trust, product lifecycle, acquisition, requirements, and international engagement. Defense contractors, AI suppliers, and DoD program offices must align development, testing, and deployment practices with the strategy’s milestones to maintain compliance and competitiveness.
Lines of effort overview
The strategy defines six lines of effort (LOEs): (1) Governance—establishing RAI oversight structures and policy frameworks; (2) Warfighter Trust—ensuring operators understand AI capabilities and limitations; (3) Acquisition and Requirements—embedding RAI in procurement processes; (4) Advance and Protect RAI Technology—investing in technical tools for trustworthy AI; (5) Responsible AI Ecosystem—training the workforce and engaging partners; and (6) International Partnerships—aligning with allies. Each LOE includes objectives, tasks, and target timelines (near-term FY2022–2023, mid-term FY2024–2025, long-term FY2026 and beyond).
Governance structures
The Chief Digital and Artificial Intelligence Office (CDAO) leads RAI governance, supported by the DoD RAI Working Council and RAI Senior Steering Group. Components must designate RAI leads and establish oversight processes. Policies will codify roles and responsibilities, integrating RAI considerations into the DoD 5000 acquisition series, the Adaptive Acquisition Framework, and risk management guidance. Contractors should monitor updates to Defense Federal Acquisition Regulation Supplement (DFARS) clauses that may require attestations or plans for RAI adherence.
Program offices must maintain documentation of AI system purpose, stakeholders, risk assessments, and approval processes. Governance artefacts should align with the AI Ethical Principles, covering user training, data provenance, model evaluation, and monitoring plans. Internal audit and inspector general offices will assess compliance, necessitating evidence repositories.
Warfighter trust and training
Building operator trust involves education, human-machine teaming protocols, and transparency. The strategy calls for training curricula that explain AI capabilities, limitations, and appropriate use cases. Programs must provide user-facing documentation, confidence measures, and fail-safe procedures. Human factors engineering should ensure user interfaces communicate uncertainty and allow intervention. Feedback loops between operators and developers are required to capture issues encountered during fielding.
The DoD plans to develop certification pathways for AI-enabled systems, potentially analogous to airworthiness or cybersecurity certifications. Contractors should anticipate requirements for usability testing, scenario-based evaluations, and evidence that human operators remain in meaningful control. Incorporate operational testing that measures operator trust, cognitive workload, and decision outcomes.
Lifecycle and technical measures
The strategy emphasises RAI across the AI lifecycle: data sourcing, model development, verification and validation (V&V), deployment, and sustainment. Programs must document data provenance, bias assessments, and privacy considerations. Technical measures include model risk management, adversarial robustness testing, explainability, and continuous monitoring. The CDAO will publish RAI toolkits, reference designs, and guidance on metrics.
Contractors should integrate responsible AI checkpoints into system engineering processes. Implement model documentation (model cards), data sheets, and test plans. Use DevSecOps pipelines with automated testing for bias, robustness, and performance drift. Deploy monitoring solutions that capture operational metrics, trigger alerts, and support retraining workflows. Align with existing DoD standards such as MIL-STD-882 (system safety), DoDI 5000.89 (test and evaluation), and cybersecurity policies (RMF, Zero Trust).
Acquisition and requirements
Acquisition authorities will embed RAI criteria into requirements documents (JCIDS) and solicitations. Requests for proposals may require RAI implementation plans, risk assessments, and evidence of adherence to AI Ethical Principles. Source selection may evaluate vendors’ governance structures, testing methodologies, and transparency commitments. Contractors should develop reusable RAI plans and compliance matrices to respond quickly to solicitations.
Requirements writers must articulate operational constraints, acceptable risk levels, and human oversight expectations. Collaboration with users is essential to translate RAI principles into measurable requirements. Align requirement documents with mission outcomes and ensure budgets include resources for testing, documentation, and sustainment of RAI processes.
Responsible AI ecosystem
Workforce development is central. The DoD will expand training via the Responsible AI Practitioner Certification (under development), integrate RAI content into Defense Acquisition University courses, and support communities of practice. Contractors should train staff on DoD RAI expectations, including legal, ethical, and technical dimensions. Document competencies and maintain training records for audit.
The strategy encourages collaboration with academia, industry consortia (Partnership on AI, Credo AI initiatives), and federally funded research and development centers (FFRDCs). Participation in research grants, pilot projects, and standards bodies can position organisations to influence RAI implementation details.
International cooperation
Allied alignment is vital for interoperability. The DoD will coordinate RAI policies with NATO, the Global Partnership on AI, and bilateral partners. Contractors operating internationally should monitor export control considerations, NATO STANAG development, and allied certification requirements. Share best practices and ensure solutions accommodate coalition data sharing, governance, and sovereign constraints.
Implementation roadmap
Near-term (FY2022–FY2023): Components must designate RAI leads, establish governance boards, and inventory AI use cases. Programs should conduct self-assessments against the RAI principles, identify gaps, and prioritise remediation. Develop initial RAI implementation plans for major AI programs, covering data management, testing, and user training.
Mid-term (FY2024–FY2025): Integrate RAI checkpoints into acquisition milestones, expand tooling (bias detection, model evaluation), and institutionalise workforce training. Deploy monitoring dashboards that capture operational metrics and compliance status. Update contracts to include RAI requirements and performance incentives.
Long-term (FY2026+): Mature continuous improvement cycles, leveraging operational feedback to refine models and governance. Align RAI practices with evolving legal frameworks (e.g., potential congressional mandates, DoD directives). Expand international interoperability testing and joint exercises focusing on RAI-enabled systems.
Risk management and assurance
Programs must integrate RAI into risk management frameworks. Identify risks such as data bias, adversarial attacks, mission failure due to model errors, and lack of operator trust. Develop mitigation strategies—diverse data sourcing, adversarial training, robust fallback modes, and human-in-the-loop controls. Maintain risk registers linked to acquisition documentation and program baselines.
Independent verification and validation (IV&V) will play a critical role. Establish review boards with legal, ethical, and technical expertise to evaluate AI systems before deployment. Prepare for Defense Inspector General or Government Accountability Office reviews focusing on RAI adherence. Maintain audit trails for data lineage, model changes, and decision logs.
Contractor action items
Vendors should update quality management systems to include RAI processes, integrate ethical risk assessments into design reviews, and ensure supply chain partners meet data and model governance standards. Develop transparency artefacts (technical documentation, user guides) tailored to DoD expectations. Implement secure development practices aligned with the DoD Software Modernization Strategy and DevSecOps reference designs (Platform One).
Engage early with contracting officers to clarify RAI expectations, propose measurable metrics, and align deliverables. Offer training and support packages for warfighters, including simulation environments and on-demand resources. Monitor budget documents (e.g., President’s Budget, Future Years Defense Program) for RAI funding priorities and align capture strategies accordingly.
Integration with broader policies
The RAI Strategy complements DoD’s AI Strategy (2018), Data Strategy (2020), and Software Modernization Strategy (2022). Ensure program roadmaps reflect dependencies between data availability, cloud infrastructure (Joint Warfighting Cloud Capability), and AI capabilities. Align with the DoD Responsible AI Tenets Implementation Guidance, when released, and with emerging federal directives on AI governance (OMB, Office of Science and Technology Policy).
By embedding responsible AI practices across governance, technology, and operations, defense organisations and suppliers can meet DoD expectations, reduce ethical and operational risks, and enable trusted adoption of AI in mission-critical contexts.
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