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Will Machines Ever Learn to Be Fair?

Will machines ever learn to be fair? Should they? What will it take? Who will decide what that means? And what will the consequences be if they're not made to be fair?

BOULDER CREEK, Calif., May. 03, 2019 – 

Why are we talking about AI fairness? Because AIs are increasingly making decisions about people:

AIs are not always accurate. There are several reasons for that, but one of the most insidious is that the datasets used for training AIs can start out flawed. If the humans who generated the data have inherent biases, those biases will end up institutionalized in the data. The decisions that AIs programmed with innate bias will sometimes be unfair. Black-box algorithms based on that data will reflect and sometimes emphasize that bias, all unbeknownst to the developer. Unless, that is, developers think ahead and consciously use methodologies to prevent bias. If there's one area where "Did you think of that?" applies, this is it.

The concept of AI and machine learning fairness is not new. Nor is awareness of the problem of AI bias and ethics. The questions surrounding these notions have been brewing for some time in the AI community, and began to be discussed more publicly, and more frequently, about two years ago. Several widely-publicized machine learning boo-boos last year – some funny, some fatal – stemming from AI bias brought the topic in front of many more people in the technical world. It even arrived in business magazines like Fortune and Forbes. This week, AI ethics was the topic of the keynote speech at the AI Everything Summit in Dubai.

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