From the course: Foundations of Responsible AI
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Systematic approaches to bias detection
From the course: Foundations of Responsible AI
Systematic approaches to bias detection
- Bias in AI systems is a critical issue, and it's not just a question of individual features or flawed inputs. It's often a product of how systems are built, evaluated, deployed. Detecting bias requires more than occasional testing. It calls for a systematic approach, one that's built into the development cycle from the very beginning. It means starting by defining what kinds of disparities matter for the use case, and then setting up methods to check for them as part of your standard evaluation process. It means looking not just at aggregate performance, but at how the system behaves across specific subgroups, across input types, across use conditions. Bias detection is also not a one-time review. It has to show up in how you sample and validate data, in how you structure your test sets, how you review failure cases. You know, the goal is never to prove that the system is fair. The goal is to understand where it behaves unevenly and why. And when that information is visible to…
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