If you don’t then we don’t blame you as this actually happened back in July 2015. Once discovered, the company issued an apology after acknowledging the sensitivity and gravity of the error.
It seems that the company went around to fix the problem but according to a report by Wired, the fix did not go beyond quickly patching the issue at hand. Instead of fixing the problem by teaching its algorithm the difference between coloured people and gorillas, the company went around to fix the problem at hand by directly removing gorillas from the image-labelling technology.
It seems that the company has simply blocked its algorithm from identifying gorillas to ensure that history does not repeat itself.
The thing to note here is that the company employed this workaround even after making it evident that image recognition will be the spine of most artificial intelligence operations like self-driving cars, personal assistants and other products.
Wired tried a number of tests to check the image recognition algorithm ranging from using Google Lens and Google Photos to try and recognise 40,000 images with a variety of subjects and objects. The system refused to identify chimps, gorillas, chimpanzee or monkey. What is interesting is that Google Assistant correctly identified a gorilla as a gorilla. In fact, the Cloud Vision API, a service that Google's Cloud computing division offers to businesses, was also able to identify chimpanzees and gorillas.
According to another test, the algorithm did not serve any results to the term “African American” while only giving results of black and white coloured images for terms such as “black man”, “black woman” and “black person”.
Google Photos, y'all fucked up. My friend's not a gorilla. pic.twitter.com/SMkMCsNVX4
— Jacky Alciné (@jackyalcine) June 29, 2015
Google issued a statement to Wired confirming that the ‘gorilla’ term was censored from the search and image tags after the incident. The representative added, “Image labelling technology is still early and unfortunately it’s nowhere near perfect.” The report goes in on more detail about the research conducted while investigating about how far Google went in fixing the problem This issue highlights the complexities and potential problems when it comes to image identification and detection algorithms. However, regardless of the problems, it is unclear on why the search giant has not been able to make a more comprehensive solution to this instead of the fix.