An independent, multi-stakeholder AI Standards Board could provide more effective oversight by creating adaptable, context-specific standards...

...similar to those in safety-critical industries like aviation and pharmaceuticals. This would address risks associated with AI and also promote public trust and ensure consistent, ongoing oversight

Asking ChatGPT-4o: Please read the paper “The necessity of AI audit standards boards” and tell me what it says. List the most surprising, controversial and valuable statements made and conclude with all best practices for all relevant stakeholders.

ChatGPT-4o’s answer:
Summary of the Paper "The Necessity of AI Audit Standards Boards"

The paper argues for the creation of an AI Audit Standards Board to address the rapidly evolving and high-risk nature of artificial intelligence (AI) systems. It highlights the inadequacy of current approaches, including internal audits and industry-led standards, which often fall short in terms of transparency and credibility. The authors suggest that an independent, multi-stakeholder board could provide more effective oversight by creating adaptable, context-specific standards similar to those in safety-critical industries like aviation and pharmaceuticals. This would not only address the risks associated with AI but also promote public trust and ensure consistent, ongoing oversight.

Most Surprising, Controversial, and Valuable Statements

1. Surprising Statements:

   • Audits should focus on the entire AI lifecycle, including the development process, not just the final product, to identify risks that might not be apparent from outputs alone.

   • Internal audits alone are insufficient, as they can become "safety-washing" without external validation and stakeholder input.

   • The rapid evolution of AI requires standards that are flexible and continuously updated, unlike the static rules typical in many other industries.

2. Controversial Statements:

   • The paper criticizes attempts by companies like OpenAI to control their own auditing processes, arguing that this approach undermines transparency and trust.

   • It questions the effectiveness of industry-led frameworks, such as Microsoft's Responsible AI Maturity Models, as potentially serving more to preempt regulation than to provide genuine oversight.

   • The concept that AI audit standards should prioritize processes over specific technical requirements, to avoid the risks of regulatory capture and outdated standards, is a departure from traditional regulatory approaches.

3. Valuable Statements:

   • A multi-stakeholder approach, involving developers, auditors, regulators, and the public, is critical to creating meaningful and broadly accepted audit standards.

   • The paper argues that audit standards boards should provide ongoing guidance rather than static rules, reflecting the dynamic nature of AI systems.

   • It emphasizes the need for transparency and public accountability in AI audits to prevent both regulatory capture and safety-washing, ensuring long-term industry stability.

Best Practices for Relevant Stakeholders

For AI Developers:

• Engage with external auditors to improve transparency and credibility.

• Implement continuous, lifecycle-based audits that go beyond final product evaluations.

• Support safety culture within the organization to align business success with ethical outcomes.

For Regulators:

• Collaborate with independent audit boards to create adaptable, principles-based standards.

• Avoid over-reliance on industry-led frameworks that may prioritize business interests over public safety.

• Require public transparency for audit processes and outcomes to build public trust.

For Auditors:

• Adopt flexible, context-specific methods for assessing AI systems.

• Maintain independence from the companies being audited to prevent conflicts of interest.

• Focus on the entire lifecycle of AI development, including training data and intermediate checkpoints.

For the Public and Civil Society:

• Advocate for broad stakeholder involvement in the standards-setting process.

• Push for transparency in both audit methods and outcomes to hold AI developers accountable.