January 2023
Intermediate to advanced
272 pages
4h 8m
English
Over the past several years, concerns around AI ethics have gone mainstream. The concerns, and the outcomes everyone wants to avoid, are largely agreed upon and well documented. No one wants to push out discriminatory or biased AI. No one wants to be the object of a lawsuit or regulatory investigation for violations of privacy. But once we’ve all agreed that biased, black-box, privacy-violating AI is bad, where do we go from here? The question most every senior leader asks is: How do we take action to mitigate those ethical risks?
Acting quickly to address concerns is admirable, but with the complexities of machine learning, ethics, and their points of intersection, ...
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