Question
A data science team explains that their new model 'gets better with more examples' rather than following logic an engineer explicitly wrote. Which concept does this describe, and how does it change what your governance program must document about the system compared to traditional software?
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This describes machine learning, a subset of AI in which a system improves its performance on a task through exposure to data rather than through explicit, human-authored rules. Because the model's behavior is learned from data rather than programmed directly, governance documentation must shift focus: instead of documenting logic and code paths the way traditional software review would, the program must document the training data's sources, quality and representativeness, since that data now functions as the de facto specification for how the system will behave, and a flaw in the data becomes a flaw in the system in a way traditional code review would never catch.
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