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Everett Thornton Smith

Everett Thornton Smith

AI & national security policy researcher; AI governance and evaluation

everett-smith
Washington, District of Columbia
Joined October 2025
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Summary

Everett is an early-career AI governance researcher focused on AI evaluation and external scrutiny for frontier models, contributing to multi‑author GovAI and RAND outputs that propose frameworks and standards (e.g., ASPIRE, GPAI evaluation desiderata) to make high‑risk model development more transparent and auditable. arxiv+2
He operates at the research–policy interface: affiliated with Georgetown University academically (confirmed on OpenReview) and contributing as a RAND Technology & Security Policy fellow, producing practitioner‑facing proposals and working papers intended to influence evaluation standards and regulatory practice. openreview+1
His background includes hands‑on experience across sectors (research fellowships at GovAI, published co‑authorship on arXiv/GovAI reports, and internships/collaborations with industry groups), indicating an ability to work on both technical evaluation methods and policy recommendations for national‑security relevant AI capabilities. governance+2

Work

Education

Projects

Writing

Toward Best Practices for AI Evaluation and Governance (RAND Perspective)

January 1, 2025

RAND expert insights piece proposing best-practice approaches and a standards taskforce for GPAI model evaluation in the EU; Everett Smith is listed among the authors/contributors.

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GPAI Evaluations Standards Taskforce: Towards Effective AI Governance

October 1, 2024

Working paper / workshop submission proposing desiderata and a proposed EU taskforce to set standards for GPAI (general-purpose AI) evaluations (internal validity, external validity, reproducibility, portability).

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Open Problems in Technical AI Governance

July 1, 2024

Multi-author report/catalog enumerating open technical research problems relevant to AI governance, covering topics from data and compute to evaluation and deployment; Everett Thornton Smith is listed among contributors/authors.

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Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework

November 1, 2023

Paper describing requirements and an ecosystem for effective external scrutiny (red‑teaming, auditing, external researcher access) of frontier large language models; introduces the ASPIRE framework (Access, Searching attitude, Proportionality, Independence, Resources, Expertise).

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