Strengthening AI safety evaluation through independent third-party review

Strengthening AI safety evaluation through independent third-party review - Amazon Science

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Conversational AI

Strengthening AI safety evaluation through independent third-party review

How Amazon partnered with AVERI to independently assess — and improve — our internal safety testing of Amazon models.

By Rahul Gupta , Jwala Dhamala

October 20, 2026

3 min read

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Listen • 5:28 min

Key takeaways

Amazon partnered with the independent nonprofit AVERI (AI Verification and Evaluation Research Institute) to conduct a third-party review of Amazon's internal safety-evaluation datasets — examining the quality of the tests themselves, not just model outputs. AVERI's independent review (conducted on a random sample of prompts, without input from Amazon's evaluation team) validated three strengths — comprehensive topical coverage across documented safety categories, diversity of testing approaches, and low contamination risk — while surfacing improvement areas such as broader multilingual coverage and more multistep adversarial prompts. In response, Amazon made four concrete changes: refreshing the test set with harder adversarial scenarios, introducing a tiered difficulty-calibration framework, establishing a quarterly review cadence, and expanding multilingual prompts evenly across all risk categories.

Was this answer helpful?

As frontier AI models grow in capability, so does the importance of ensuring that safety evaluations are rigorous and comprehensive. At Amazon, we invest heavily in internal safety testing for our models, spanning categories like harmful-content generation, cybersecurity risks, privacy protection, and copyright compliance.

AVERI’s mandate wasn’t just to test the outputs of Amazon models but to examine the quality and rigor of Amazon's own tests.

A core principle of robust quality assurance is that even rigorous internal processes are strengthened by external scrutiny. That's why we partnered with AVERI (AI Verification and Evaluation Research Institute), an independent nonprofit dedicated to third-party evaluation of AI systems, to conduct an independent review of our internal safety evaluation datasets. AVERI’s mandate wasn’t just to test model outputs but to examine the quality and rigor of our tests themselves — asking whether our evaluations stress-test safety boundaries.

How the collaboration worked

We provided AVERI with a random sample of prompts drawn from our internal safety-evaluation suite. AVERI's team — led by executive director Miles Brundage and including researchers Sean McGregor, A. Feder Cooper, and George Balston — examined the evaluation prompts for diversity, difficulty calibration, and methodological soundness. AVERI assessed the sample independently without input from our evaluation team, ensuring their findings reflected a genuine external perspective.

What the review confirmed

AVERI’s review validated several strengths of our evaluation process:

Comprehensive topical coverage — aligned with publicly documented safety categories including self-harm, cybersecurity, and other risk areas; Diversity...