Accuracy Fallacy: The Media’s Coverage of AI Is Bogus

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KDnuggets
Accuracy Fallacy: The Media’s Coverage of AI Is Bogus
Stanford’s “Gaydar” Doesn’t Perform at Face Value
Breaking News: Psychotic Breaks Are Still Mostly Unpredictable
Accuracy: A Word So Often Used Inaccurately
The Accuracy Fallacy

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With articles like these, the press will have you believe that machine learning can reliably predict whether you’re gay, whether you’ll develop psychosis, whether you’ll have a heart attack, and whether you’re a criminal – as well as other ambitious predictions such as when you’ll die and whether your unpublished book will be a bestseller.
In its opening summary (the “abstract”), the university’s 2018 report claims their predictive model achieves 91% accuracy distinguishing gay and straight males from facial images.
Now, the researchers did indeed report on a viable measure of predictive performance, called AUC – albeit mislabeled in their report as “accuracy.” AUC (a.k.a., AUROC, Area Under the Receiver Operating Characteristic curve) is a single value that indicates to the researcher the extent of performance trade-offs their predictive model is capable of.
“Machine learning algorithms can help psychologists predict, with 90% accuracy, the onset of psychosis by analyzing a patient’s conversations.” Thus opens an article in The Register (U.K.) eagerly covering an overzealous report out of Emory and Harvard Universities.
The Global Times (China) ran the headline, “Professor Claims AI Can Spot Criminals by Looking at Photos 90% of the Time.” Also reporting on this work, in which a model predicts criminality based on facial features, MIT Technology Review and The Telegraph (U.K.) each repeated the 90% accuracy claim.
One headline claimed, “Google AI Predicts Hospital Inpatient Death Risks with 95% Accuracy.” Google researchers published this result in Nature, leading the press astray by leaving it implied within the summary that AUC is a way to measure accuracy.
The press reported on a model “that predicted suicide risk, using electronic health records, with 84 to 92% accuracy within one week of a suicide event

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