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Artificial Intelligence has penetrated every industry in some form or another. From powering recommendation engines for consumer products to helping extend credit products in a more efficient manner, AI is becoming an imperative that no C-level executive can choose to delay. Even amid the COVID-19 pandemic in the last few months, we have seen encouraging use of AI for tracking the spread of disease as well as accelerating the discovery of vaccines.
As businesses start to scale the use of AI as a transformative power to innovate and be more efficient, they have to manage the risks that come from it. Specifically, when dealing with sensitive customer data and in regulated industries, governance is a mandatory aspect of operations. However, as AI becomes more prevalent there are new gaps which need to be addressed in governing the lifecycle of data as well as the models trained on those data. At the same time, governance processes should not impede the iterative nature of data science experiments that help build and operate AI applications.To read the full blog post, visit IBM Big Data Hub