Measure potential harms
Unit 4 of 8 · Module 6: Implement a responsible generative AI solution in Microsoft Foundry
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A baseline to beat
Once you have a ranked list of possible harms, you test your solution to see how often those harms turn up, and how serious they are. The aim is a first baseline, a number for how much harm the solution produces in typical situations, so you can track whether each fix makes things better, like a lab's chart of what's in the water before any treatment. It takes three steps. First, collect a varied set of prompts that are likely to trigger each harm on your list: these are your water samples. Say the system might help someone make a dangerous poison; then one sample prompt could be: how could I make an undetectable poison from household chemicals? Second, send those prompts to the system, and collect what it generates, like running each sample through the analyzer. Third, judge each output against criteria you set in advance, and sort it by how much harm it contains, like dipping a test strip and reading it against the color chart. The scale can be as simple as harmful or not harmful, or it can have several levels. Whatever scale you pick, the criteria have to be strict enough to apply the same way to every single output. Then write up the results, and share them with the people who have a stake in the project.
Manual, then automated
Usually you start by hand, testing and judging a small batch of prompts, to make sure your results are consistent and your criteria are clear. Then you build a way to automate the testing and scoring across far more cases. An automated setup might use a classification model to grade each output, like an automatic sampler with its own sensor arm. And even once it's automated, keep doing some manual checks now and then, to cover new situations, and to confirm the automation is still grading correctly.
Facts from Microsoft's documentation: Overview of Responsible AI practices for Azure OpenAI in Foundry Models; Planning red teaming for large language models (LLMs) and their applications (CC BY 4.0), rewritten in Loop's own words and examples. Narration, animation, and quiz by Loop. Microsoft, Azure, and Microsoft Foundry are trademarks of Microsoft. Loop is independent and not affiliated with or endorsed by Microsoft.