摘要: The models you create have real-world applications that affect how your colleagues do their jobs. That means they need to understand what you’ve created, how it works, and what its limitations are. They can’t do any of these things if it’s all one big mystery they don’t understand. “I’m afraid I can’t let you do that, Dave… This mission is too important for me to let you jeopardize it”

 

 

Ever since the spectacular 2001: A Space Odyssey became the most-watched movie of 1968, humans have both been fascinated and frightened by the idea of giving AI or machine learning algorithms free rein.

In Kubrick’s classic, a logically infallible, sentient supercomputer called HAL is tasked with guiding a mission to Jupiter. When it deems the humans on board to be detrimental to the mission, HAL starts to kill them.

This is an extreme example, but the caution is far from misplaced. As we’ll explore in this article, time and again, we see situations where algorithms “just doing their job” overlook needs or red flags they weren’t programmed to recognize.

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