Ronald J. Bodkin

Titles as the sources gave them. In 1 entry, with 5 checked quotations.

What they said

Newest first. Each quotation was found word for word in the source.

“We propose comprehension audits, a novel development-process assurance mechanism in which the responsible people explain R&D contributions to auditors to demonstrate understanding.”
Comprehension Audits to Mitigate Risks from Automated AI ResearcharXiv, October 7, 2026. Speaking as: Other. The paper's abstract, introducing its proposal.
“Our analysis of leading open-source AI projects finds increased output of code with reduced human review commentary rates per line of code, with far lower rates for automated fleet accounts.”
Comprehension Audits to Mitigate Risks from Automated AI ResearcharXiv, October 7, 2026. Speaking as: Other. The abstract, summarising the paper's study of open-source AI code repositories.
“Ultimately, approval of AI-written output without comprehension is meaningless.”
Comprehension Audits to Mitigate Risks from Automated AI ResearcharXiv, October 7, 2026. Speaking as: Other. Closing the paper's problem statement, which cites a 2024 consensus statement that AI should not improve itself without explicit human approval.
“We believe that regulations that mandate comprehension audits performed by accredited external auditors are a promising medium-term intervention to reduce risks from automated AI R&D.”
Comprehension Audits to Mitigate Risks from Automated AI ResearcharXiv, October 7, 2026. Speaking as: Other. The paper's final sentence, after calling for a pilot at a frontier lab.
“Ultimately, comprehension audits provide an early warning and a safety brake. They don’t make automated AI R&D safe through alignment or ensure control.”
Comprehension Audits to Mitigate Risks from Automated AI ResearcharXiv, October 7, 2026. Speaking as: Other. End of the authors' Limitations section.

In the Base

Every entry they appear in.

  1. ResearcharXivWrote it

    Comprehension Audits to Mitigate Risks from Automated AI Research

    In an arXiv preprint, Ronald J. Bodkin, Bahrad A. Sokhansanj and Gillian Hadfield propose 'comprehension audits': independent auditors check, without AI help, that the people behind AI-lab research understand it, and work pauses if they cannot. Their study of open-source AI projects finds more code and fewer human review comments per line. They note the audits are untested in practice.