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Friday, February 22, 2019

DeRisking Analytics?

Risk exists in all kinds of analytics, from Regression to Optimization to 'AI'.   But it also increases with a number of factors.   Investments made, management expectations, exposure of decisions to the public,  and lots more.    All these are changing in context constantly.   McKinsey suggests 'Validation Frameworks',  usefully stated in the article below.   We never used that term, and were usually not that formal,  but the downside risk of complex, not very non-transparent methods that can look like dangerous bias, may require it.  Regulations may soon specify it.    Reducing Risk sounds more accurate than de-Risking.   Risk is always there.

Derisking machine learning and artificial intelligence
The added risk brought on by the complexity of machine-learning models can be mitigated by making well-targeted modifications to existing validation frameworks.     in McKinsey.  .... "

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