It is no surprise that the introduction of AI systems also introduces the risk of bias, inequality, and responsible AI failures, on top of privacy and security concerns. Surprising, though, is the rate of the rapid proliferation of the AI Risk Management ecosystem: twenty-one draft standards and frameworks were published last year from standards organizations last year alone, and already large companies and an emerging group of startups offer tests to measure AI systems against these standards. As these frameworks are finalized, soon, organizations will adopt these standards and eventually compliance officers, ML engineers and security professionals will have to apply them to their AI systems.
In this talk, using qualitative studies and experimental work, we show how two popular AI Risk Management frameworks – NIST AI RMF and the draft EU AI Act – despite their best intentions, have five roadblocks that stakeholders will face when operationalizing these standards, specifically from the vantage point of security....
By: Jonathon Penney , Ram Shankar Siva Kumar
Full Abstract and Presentation Materials: #risks-of-ai-risk-policy-five-lessons-32619
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