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AI challenges- KM contribution

1 July 2021

Dr. Moria Levy

KMGN KM-AI course, session 16. Dr. Art Murray leads us through an enlightening session, focusing on AI challenges and risks and suggesting aspects and issues where KM can contribute. Art mapped the various stages, the main AI challenges, and how KM can practically be embedded to address the risks and challenges.


The most important recommendation, in my opinion, addresses the main five known data attributes, known as the five “V's” of big data. These five attributes, how cynical, serve both as the big-data promise and the significant challenges. If we read the list- volume, variety, velocity, veracity, and value, it is not too complicated to see that knowledge management has a great deal to offer. KM helps to reduce confusion by incorporating human judgment and sensemaking; KM helps to establish thresholds and parameters for placing human decision making as part of the AI process; KM helps to incorporate semantic and situational context into the data, thus giving it structure and value; KM may establish crumb trails into the knowledge connecting result to source; and KM helps in the so important task of aligning business needs and strategies with AI technological doing.


I admit that even if the contribution ended here, it would be much more than enough. KM offers even much more. Again I thank my luck in working in such a great discipline 😊



 This post was initially published in LinkedIn


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