Episode 60 — Quantify AI Risk When Possible: Likelihood, Impact, and Confidence Ranges (Domain 2)
While qualitative assessments are useful for ethics, many AI risks can and should be quantified to provide more precise guidance for decision-makers in Domain 2. This episode covers the methods for quantifying risk by estimating the likelihood of an AI failure and the range of its potential financial impact. For the AAIR certification, you must understand how to use statistical distributions and "confidence ranges" to express the uncertainty inherent in AI systems. We explore how to calculate the cost of a model error—such as an incorrect credit limit—and how to weigh that cost against the potential benefits of the automation. We also discuss the limitations of quantification, particularly when historical data for "black swan" AI events is scarce. Using quantitative metrics allows risk managers to rank AI projects objectively and demonstrate the ROI of risk mitigation efforts to the board. Mastering these skills ensures that you can provide the rigorous, data-backed analysis that modern enterprises demand from their risk leaders. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
