From the course: Foundations of Responsible AI
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Critical decision points in AI development
From the course: Foundations of Responsible AI
Critical decision points in AI development
- Every AI development cycle includes a set of decisions that determine how the system will behave. Some of these are obvious, selecting training data, setting thresholds, evaluating outputs, but others can be harder to see and sometimes they carry just as much weight. These include choices about which metrics to prioritize, which cases to test, and how we define not just success, but failure across different user groups. So let's focus on the specific points in the technical workflow where decisions tend to have the greatest impact. And the goal here is to make those moments visible so they can be approached with structure and with consistency. We start with considering data sourcing and labeling. The training data defines what the model learns and what it ignores. And this includes not just what examples are included, but how they're annotated, how edge cases are handled, and whether the dataset reflects the distribution of real world users that the system will encounter. A next…
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