The fear of artificial intelligence is so palpable, there's an entire school of technological philosophy dedicated to figuring out how AI might trigger the end of humanity. Not to feed into anyone's paranoia, but here's a list of times when AI caused — or almost caused — disaster.
Microsoft bot's inappropriate tweetsIn 2016, Microsoft released a Twitter bot called Tay, which was meant to interact as an American teenager, learning as it went. Instead, it learned to share radically inappropriate tweets. Microsoft blamed this development on other users, who had been bombarding Tay with reprehensible content. The account and bot were removed less than a day after launch. It's one of the touchstone examples of an AI project going sideways.
Amazon's discriminatory AI recruiting toolIn 2015, an Amazon AI recruiting tool was found to discriminate against women. Trained on data from the previous 10 years of applicants, the vast majority of whom were men, the machine learning tool had a negative view of resumes that used the word"women's" and was less likely to recommend graduates from women's colleges.
Driverless car disasterWhile Tesla tends to dominate headlines when it comes to the good and the bad of driverless AI, other companies have caused their own share of carnage. One of those is GM's Cruise. An accident in October 2023 critically injured a pedestrian after they were sent into the path of a Cruise model. From there, the car moved to the side of the road, dragging the injured pedestrian with it.
Faulty translationAI-powered translation and transcription tools are nothing new. However, when used to assess asylum seekers' applications, AI tools are not up to the job. According to experts, part of the issue is that it's unclear how often AI is used during already-problematic immigration proceedings, and it's evident that AI-caused errors are rampant.
Unwanted popularity contest Politicians are used to being recognized, but perhaps not by AI. A 2018 analysis by the American Civil Liberties Union found that Amazon's Rekognition AI, a part of Amazon Web Services, incorrectly identified 28 then-members of Congress as people who had been arrested. The errors came with images of members of both main parties, affecting both men and women, and people of color were more likely to be wrongly identified.