Modern artificial intelligence (AI) systems pose new kinds of risks, and many of these are both consequential and not well understood. Despite this, many AI-based systems are being accelerated into deployment. This is creating great urgency to develop effective test and evaluation (T&E) practices for AI-based systems. This blog post explores potential strategies for framing T&E practices on the basis of a holistic approach to AI risk. In developing such an approach, it is instructive to build on lessons learned in the decades of struggle to develop analogous practices for modeling and assessing cyber risk. Cyber risk assessments are imperfect and continue to evolve, but they provide significant benefit nonetheless. They are strongly advocated by the Cybersecurity and Infrastructure Security Agency (CISA), and the costs and benefits of various approaches are much discussed in the business media. About 70% of internal audits for large firms include cyber risk assessments, as do mandated stress tests for banks.
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