OpenAI publishes a report on mechanisms to improve verifiability in AI development, addressing how stakeholders can verify organizations' claims about AI system properties and safety practices.
We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwartz Reisman Institute for Technology and Society, Center for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to improve the verifiability of claims made about AI systems. Developers can use these tools to provide evidence that AI systems are safe, secure, fair, or privacy-preserving. Users, policymakers, and civil society can use these tools to evaluate AI development processes.
# Improving verifiability in AI development
Source: [https://openai.com/index/improving-verifiability/](https://openai.com/index/improving-verifiability/)
While a growing number of organizations have articulated ethics principles to guide their AI development process, it can be difficult for those outside of an organization to verify whether the organization’s AI systems reflect those principles in practice\. This ambiguity makes it harder for stakeholders such as users, policymakers, and civil society to scrutinize AI developers’ claims about properties of AI systems and could fuel competitive corner\-cutting, increasing social risks and harms\. The report describes existing and potential mechanisms that can help stakeholders grapple with questions like:
- Can I \(as a user\) verify the claims made about the level of privacy protection guaranteed by a new AI system I’d like to use for machine translation of sensitive documents?
- Can I \(as a regulator\) trace the steps that led to an accident caused by an autonomous vehicle? Against what standards should an autonomous vehicle company’s safety claims be compared?
- Can I \(as an academic\) conduct impartial research on the risks associated with large\-scale AI systems when I lack the computing resources of industry?
- Can I \(as an AI developer\) verify that my competitors in a given area of AI development will follow best practices rather than cut corners to gain an advantage?
The 10 mechanisms highlighted in the report are listed below, along with recommendations aimed at advancing each one\. \(See the[report\(opens in a new window\)](https://arxiv.org/abs/2004.07213)for discussion of how these mechanisms support verifiable claims as well as relevant caveats about our findings\.\)
The article discusses the industry consensus that AI is becoming extremely capable but still faces reliability issues for high-stakes tasks, emphasizing that current systems optimize for plausibility rather than guaranteed truth, and that the path forward involves layered verification systems rather than a single perfect model.
The article discusses the concept of verifiable AI inference, exploring methods like trusted attestation and cryptographic proofs to ensure the authenticity and provenance of AI-generated outputs without rerunning the model.
The article discusses the challenge of verifying AI-written code when AI also creates verification tools, highlighting human evaluation as a bottleneck and the strategic importance of training data between US and Chinese labs.
The article argues for an accountability layer in AI systems to ensure transparency and prevent over-trust, highlighting concerns about massive AI output and proposing methods to make AI behavior inspectable.