Institution profile

Arcadia Impact

Research institutionnorthamerica · us
Research library10linked papers
Opportunities0open roles
Selected work

Representative Papers

Designing escalation criteria for international AI incident response: criteria, triggers, and thresholds

Apr 25, 2026

This study addresses the absence of actionable international standards for determining when AI incidents warrant escalation from national to cross-border coordinated responses. It proposes a systematic, multi-jurisdictional escalation framework that integrates eight assessment criteria, gated decision points, and threshold mechanisms to balance local policy flexibility with global coordination. Through regulatory analysis (e.g., SB 53, EU AI Act), cross-sectoral response framework comparisons, structured case testing, and flowchart modeling, the research identifies three design patterns in developer-led reporting systems that contribute to underreporting and highlights how ambiguous definitions and data gaps critically undermine detection efficacy. Validation across ten real-world and variant incidents demonstrates the framework’s practical utility while exposing significant deficiencies in current regimes regarding timeliness and operational feasibility.

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Lessons from External Review of DeepMind's Scheming Inability Safety Case

Apr 23, 2026

This study addresses the limitations of self-produced safety arguments in frontier AI systems, which are often compromised by confirmation bias and conflicts of interest, thereby failing to ensure adequately controlled risks. For the first time, it applies a structured external review methodology to this domain, leveraging the Assurance 2.0 framework to systematically evaluate DeepMind’s safety argument concerning “incapacitation.” The analysis identifies critical risks omitted from the original argument, substantially narrowing its scope of applicability. Furthermore, the work outlines concrete pathways to enhance transparency and the effectiveness of external scrutiny, offering actionable guidance for both AI developers and regulatory bodies on implementing rigorous, independent safety assessments.

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Recent publications

Latest Papers

Designing escalation criteria for international AI incident response: criteria, triggers, and thresholds

Apr 25, 2026

This study addresses the absence of actionable international standards for determining when AI incidents warrant escalation from national to cross-border coordinated responses. It proposes a systematic, multi-jurisdictional escalation framework that integrates eight assessment criteria, gated decision points, and threshold mechanisms to balance local policy flexibility with global coordination. Through regulatory analysis (e.g., SB 53, EU AI Act), cross-sectoral response framework comparisons, structured case testing, and flowchart modeling, the research identifies three design patterns in developer-led reporting systems that contribute to underreporting and highlights how ambiguous definitions and data gaps critically undermine detection efficacy. Validation across ten real-world and variant incidents demonstrates the framework’s practical utility while exposing significant deficiencies in current regimes regarding timeliness and operational feasibility.

0 citationsRead paper

Lessons from External Review of DeepMind's Scheming Inability Safety Case

Apr 23, 2026

This study addresses the limitations of self-produced safety arguments in frontier AI systems, which are often compromised by confirmation bias and conflicts of interest, thereby failing to ensure adequately controlled risks. For the first time, it applies a structured external review methodology to this domain, leveraging the Assurance 2.0 framework to systematically evaluate DeepMind’s safety argument concerning “incapacitation.” The analysis identifies critical risks omitted from the original argument, substantially narrowing its scope of applicability. Furthermore, the work outlines concrete pathways to enhance transparency and the effectiveness of external scrutiny, offering actionable guidance for both AI developers and regulatory bodies on implementing rigorous, independent safety assessments.

0 citationsRead paper