:format(webp)/cdn.vox-cdn.com/uploads/chorus_image/image/63709683/google-photos.0.1462605712.0.png)
In May, Google unfurled its new Photos app as the crowning achievement of machine learning capabilities — a service that stores and catalogues your images with computing smarts that can pick out buildings, landscapes, animals, even abstract events like birthdays, on its own.
As users get their hands on the app, though, it’s evident Photos is far from perfect. Two days ago, Jacky Alciné, an African-American programmer based in New York, flagged a flagrant error on Photos: It had tagged him and his friend as “gorillas.”
Google Photos, y’all fucked up. My friend’s not a gorilla. pic.twitter.com/SMkMCsNVX4
— diri noir avec banan (@jackyalcine) June 29, 2015
To Google’s credit, its human ambassadors responded swiftly. Yonatan Zunger, an engineer and “chief architect” of Google+, the social service from which Photos was stripped, replied to Alciné on Twitter within roughly 90 minutes, noting that he had alerted the Photos team.
@jackyalcine Holy fuck. G+ CA here. No, this is not how you determine someone’s target market. This is 100% Not OK.
— Yonatan Zunger (@yonatanzunger) June 29, 2015
Zunger checked in with Twitter missives the following day; Alciné thanked him, and noted that the erroneous label was removed.
Google has advanced more on artificial intelligence than others, infusing the advanced tech into speech and photo recognition as well as natural language processing. (Its trippy neural network construction has yet to be baked into consumer products.) The company is frank that its machine learning abilities still have a way to go. In his exchange on Twitter, Zunger noted how the company was still working on “long-term fixes” for linguistics and image recognition for “dark-skinned faces.”
Google put out this conciliatory statement: “We’re appalled and genuinely sorry that this happened. We are taking immediate action to prevent this type of result from appearing. There is still clearly a lot of work to do with automatic image labeling, and we’re looking at how we can prevent these types of mistakes from happening in the future.”
Zunger, for his part, put it more bluntly.
@wiselar @louisgray @jackyalcine I wish it were. Until recently, it was confusing white faces with dogs and seals. Machine learning is hard.
— Yonatan Zunger (@yonatanzunger) June 30, 2015
This article originally appeared on Recode.net.
