AI and Social Impact

Abstract

Just because everything can be automated does not mean everything should be. As machine learning and artificial intelligence become intertwined within the global fabric of society, potential societal impacts must be considered. Is it necessary to know someone's sexual orientation? Will that help sell products or pose a threat to that individual? Countries call for such algorithms; however, the literature has shown that current attempts are neither significant nor correct most of the time. Is it important to know one's race? What happens when a person of color is targeted based on biased algorithms by police? Or denied a loan based on biased resourcing that indicates low-income individuals are more likely to offend? These algorithms contain a multitude of bias based on the datasets used. The use of inclusive datasets is necessary to get accurate, unbiased, and therefore viable data to ensure that AI technologies function correctly.

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