Uber Could Detect if Prospective Passenger is Wasted

The patent is titled “predicting user state using machine learning,” so it doesn’t explicitly mention drunkenness or drug use. But its contents, which speak of “uncharacteristic user states” and “identifying a normal or abnormal state of the respective user,” suggest such matters may be at the center of the idea. Other conditions that could also conceivably lead to uncharacteristic behavior by a rider could also include extreme tiredness.

According to the filing, the way it works is that A.I. would be built into the app and would monitor behavior such as typing speed and accuracy, as well as walking speed and direction, before interpreting the data to determine whether the rider sober or not. So if you’re typing slower than usual, making more mistakes than you ordinarily do, and perhaps dropping the phone or staggering, the software is likely to conclude that you’re intoxicated.

The algorithm could also make use of time and location data, taking special note of whether or not the ride request is coming from an entertainment area with bars and clubs at the end of the night.

If the system concludes that the rider is intoxicated, it could deal with the Uber request in a number of ways. For example, it might match the compromised rider with a more experienced driver, or one trained to handle such potentially disorderly passengers. It might also prevent drunk riders from taking a pooled ride. At the very least, it would serve as notice to the Uber driver that the rider may be an unsavory character. Of course, if the algorithm detects someone in a particularly bad state, a driver may simply refuse to pick them up .

Some Uber riders, however, may find the idea expressed in Uber’s patent somewhat troubling, especially as it could offer a way for predatory drivers to target vulnerable riders who may not be in full control of their faculties.

Privacy advocates, too, may not like the sound of it. Uber doesn’t have the best track record when it comes to handling customer data, and holding information on when its riders are perceived to be drunk or sober may leave some riders feeling uncomfortable.

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