One of the most exciting AWS re:Invent 2020 announcements was a new Amazon SageMaker feature, purpose built to help detect bias in machine learning (ML) models and explain model predictions: Amazon SageMaker Clarify. In today’s world where predictions are made by ML algorithms at scale, it’s increasingly important for large tech organizations to be able to explain to their customers why they made a certain decision based on an ML model’s prediction. Crucially, this can be seen as a direct move away from the underlying models being closed boxes for which we can observe the inputs and outputs, but not the internal workings. This not only opens up avenues of further analysis, so as to iterate and further improve on model configurations, but also provides previously unseen levels of model prediction analysis to customers.
One particularly interesting use case for Clarify is from the Deutsche Fußball Liga (DFL) on Bundesliga

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