Implementing independent auditing, evaluations, and licensing before AI models are released to the market can help identify potential risks and flaws. However, the effectiveness of this approach depends on the capabilities and expertise of auditors and the ability to stay ahead of rapidly evolving AI systems. Auditing systems that learn and adapt in real time presents additional challenges in terms of monitoring and assessment. While auditing AI systems is a good idea in theory, the lack of comprehensive understanding and the exponential growth of AI capabilities raise concerns about the efficacy of audits. Therefore, more research, investment, and talent are needed to address these challenges. The potential for audit washing, where a model is considered safe solely based on passing an audit, adds to the uncertainty. The constantly changing nature of AI models and the difficulty in determining their capabilities are alarming, calling for caution in rolling out these technologies.

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