7 Revealing Ways AIs Fail

Neural networks can be disastrously brittle, forgetful, and surprisingly bad at math

8 min read
Chris Philpot

This article is part of our special report on AI, “The Great AI Reckoning.

Artificial intelligence could perform more quickly, accurately, reliably, and impartially than humans on a wide range of problems, from detecting cancer to deciding who receives an interview for a job. But AIs have also suffered numerous, sometimes deadly, failures. And the increasing ubiquity of AI means that failures can affect not just individuals but millions of people.

Increasingly, the AI community is cataloging these failures with an eye toward monitoring the risks they may pose. "There tends to be very little information for users to understand how these systems work and what it means to them," says Charlie Pownall, founder of the AI, Algorithmic and Automation Incident & Controversy Repository. "I think this directly impacts trust and confidence in these systems. There are lots of possible reasons why organizations are reluctant to get into the nitty-gritty of what exactly happened in an AI incident or controversy, not the least being potential legal exposure, but if looked at through the lens of trustworthiness, it's in their best interest to do so."

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