IBM stirs controversy over facial recognition
IBM’s original intentions rooted in preventing biases in AI
The use of data to train an artificial intelligence has always been a tricky topic to begin with. Now, IBM seems to have landed itself in a controversy.
According to reports, IBM seems to have used almost a million photos from photos sharing website Flickr, to train its artificial intelligence for facial recognisition and shared them with outside researchers. However, the controversy, according to report on NBC, revolves on the fact that the people photographed on Flickr didn’t consent to have their photos used to develop a facial recognisition system.
The report went on to state that the photographers may have taken the necessary permission to take the pictures but didn’t inform that the photos may be used to train an artificial intelligence.
“None of the people I photographed had any idea their images were being used in this way,” one photographer told NBC.
IBM, which finds itself at the centre of the dispute, isn’t to be blamed as the photos weren’t compiled by them. Rather, the photos, which are a part of larger collection of 99.2 million photos, called as YFCC100M, were put together to conduct research by Flickr’s former owner Yahaoo.
It’d be wise to note that all the photos were shared under a Creative Commons license.
But the fact they could potentially be used to train facial recognition systems to profile by ethnicity, as one example, may not be a use that even Creative Commons’ most permissive licenses anticipated.
It’s not entirely a theoretical example: IBM previously made a video analytics product that used body cameras to figure out peoples’ races. IBM denied that it would “participate in work involving racial profiling,” it told The Verge.
It’s also worth noting that IBM’s original intentions may have been rooted in preventing AI from being biased against certain groups though — when it announced the collection in January, the company explained that it needed such a large dataset to help train for “fairness”.