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One of the emerging challenges in machine learning concerns the problem of transparency ; put simply, not all ML models are easily explainable. Historically this seem to have not been a major problem, and we often associate the term 'black box', which may even have added to the mystique surrounding AI.
The issue with black boxes is that you can't easily tell what's happening inside, which may be compounded where an ML model happens to be also non-deterministic (in fact, or for all practical purposes). So how does this problem manifest itself?
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