Operationalizing Governance for AI-Enabled Defense-Relevant Robotics: From Principles to Controls, Metrics, and Enforcement
Assessment outcome
total 4/4 (actionability 2 + authority 2); currency unscored, so pro-rated to 6.0/6
Authorship & scope
Author Savannah Atkinson and her affiliation are explicitly named.
Level 2: Published in Lecture Notes in Computer Science (peer-reviewed conference proceedings series by Springer).
Issuers, who it governs, legal force and content are on the full record.
Eligibility gates
HTTP 200, 1360 words of full text extracted
The source is entirely about AI-enabled robotic systems and their governance.
The source addresses AI governance frameworks, controls, risk management, and oversight.
Author Savannah Atkinson and her affiliation are explicitly named.
There is no indication that this current version has been superseded, repealed, or withdrawn.
Scored criteria
Actionability
2/2Level 2: Names specific obligations, controls, and duties (threat modeling, identity management, software bill of material provenance) required in design and deployment phases.
Key design-phase controls (e.g., threat modeling, identity management baselines, code testing, and software bill of material provenance) and deployment-phase measures (e.g., segmented networks, robotics-tailored monitoring, cryptographically signed updates, and robust logging and incident response) were identified as critical.
Authority
2/2Level 2: Published in Lecture Notes in Computer Science (peer-reviewed conference proceedings series by Springer).
Atkinson, S. (2026). Operationalizing Governance for AI-Enabled Defense-Relevant Robotics: From Principles to Controls, Metrics, and Enforcement. In: Moallem, A. (eds) HCI for Cybersecurity, Privacy and Trust. HCII 2026. Lecture Notes in Computer Science, vol 16738. Springer, Cham.
Currency
—computed: no publish/modified date found on the page (never guessed)