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Stories of AI Failure and How to Avoid Similar AI Fails

This article includes stories of recent, high-profile AI fails, as well as information and advice on how to avoid your own AI failure: Full disclosure if you’re new to Lexalytics: we provide a software platform that uses AI and machine learning to help people analyze text documents, including tweets, reviews and contracts.

The first line of the press release boldly declares, “MD Anderson is using the IBM Watson cognitive computing system for its mission to eradicate cancer.”

IBM’s role was to enable clinicians to “uncover valuable insights from the cancer center’s rich patient and research databases.”

In July 2018, StatNews reviewed internal IBM documents and found that IBM’s Watson was making erroneous, downright dangerous cancer treatment advice.

including one case where Watson suggested that doctors give a cancer patient with severe bleeding a drug that could worsen the bleeding.

The phone’s shiniest new feature was Face ID, a facial recognition system that replaced the fingerprint reader as your primary passcode.

Apple said that Face ID used the the iPhone X’s advanced front-facing camera and machine learning to create a 3-dimensional map of your face.

The machine learning/AI component helped the system adapt to cosmetic changes (such as putting on make-up, donning a pair of glasses, or wrapping a scarf around your neck), without compromising on security.

But a week after the iPhone X’s launch, hackers were already claiming to beat Face ID using 3D printed masks. Vietnam-based security firm Bkav found that they could successfully unlock a Face ID-equipped iPhone by glueing 2D “eyes”

Bkav’s claims, outlined in a blog post, gained widespread attention, not least because Apple had already written that Face ID was designed to protect against “spoofing by masks or other techniques”

As one Amazon engineer told The Guardian in 2018, “They literally wanted it to be an engine where I’m going to give you 100 résumés, it will spit out the top five, and we’ll hire those.” But eventually, the Amazon engineers realized that they’d taught their own AI that male candidates were automatically better.

In 2018, the American Civil Liberties Union showed how Amazon’s AI-based Rekognition facial recognition system According to the ACLU, “Nearly 40 percent of Rekognition’s false matches in our test were of people of color, even though they make up only 20 percent of Congress.” In fact, that’s not even the first time someone’s proven that Rekognition is racially biased.

In another study, University of Toronto and MIT researchers found that every facial recognition system they tested performed better on lighter-skinned faces.

Seriously, just read this article from The Guardian: How white engineers built racist code – and why it’s dangerous for black people Microsoft and Apple aren’t the only companies who’ve made headlines with embarrassing AI fails.

Together, these 5 AI failures cover: chatbots, political gaffs, autonomous driving accidents, facial recognition mixups, and angry neighbors.

In the rush to stay ahead of the technology curve, companies often fail to consider the impact of their inherent biases.

This is particularly dangerous for companies working in data analytics for healthcare, biotechnology, financial services and law.

Just look at Watson for Oncology: data bias and lack of social context doomed that AI project to failure and sent $62 million down the drain.

As cars become more complex, insurance companies advise owners to keep up with preventative maintenance before the cost of repairs becomes staggering.

In this article on Forbes, he examines a number of business applications for AI solutions to: “Building a business case for AI isn’t so different from building one for any other business problem,”

Jeff puts it best: “With the right business case and the right data, AI can deliver powerful time and cost savings, as well as valuable insights you can use to improve your business.”

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