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Agreeing that AI should be fair, reliable, and safe is easy. You still need evidence. This primer gives you the 8 questions to ask of any AI system, in one sitting: why did the model produce this result, does it treat groups differently, is the output good enough, how does it fail when someone tries to misuse it, what checks can prevent harm while it's running, what is it doing once people use it, what changes when it can take actions, and what evidence does someone need to approve it.
It closes with a start you can make this week: pick one system you're responsible for, work through the questions, and write down the gaps.
Written by the person who led quantitative model risk inspections and AI risk supervision at the Monetary Authority of Singapore, and wrote Singapore's AI risk management guidelines for the financial sector. About a dozen pages, deliberately boring.
This primer is the gateway to the full book, Boring Questions for AI Lifecycle Risk Management (listed here separately), which works each question in depth - what it means, what to look for, and the open-source tools to start your learning journey. If this primer was useful, pass it along.
Agreeing that AI should be fair, reliable, and safe is easy. You still need evidence. This primer gives you the 8 questions to ask of any AI system, in one sitting: why did the model produce this result, does it treat groups differently, is the output good enough, how does it fail when someone tries to misuse it, what checks can prevent harm while it's running, what is it doing once people use it, what changes when it can take actions, and what evidence does someone need to approve it.
It closes with a start you can make this week: pick one system you're responsible for, work through the questions, and write down the gaps.
Written by the person who led quantitative model risk inspections and AI risk supervision at the Monetary Authority of Singapore, and wrote Singapore's AI risk management guidelines for the financial sector. About a dozen pages, deliberately boring.
This primer is the gateway to the full book, Boring Questions for AI Lifecycle Risk Management (listed here separately), which works each question in depth - what it means, what to look for, and the open-source tools to start your learning journey. If this primer was useful, pass it along.