Be worried: Computer-vision research mostly used for surveillance

Computer-vision research can be used to spot cancerous cells, classify animal species, or in robot vision. But much of the time, the technologies are used to identify and track people, suggests a new study.

Computer-vision research, which involves developing algorithms to extract information from images and videos, almost always includes analyzing humans and their environments.

Unfortunately, most of the subsequent patents can be used in surveillance technologies, a new study published in Nature has found. These, of course, are usually deployed to identify and track people.

That’s why computer scientists need to “wake up” and consider the moral implications of their work, said Yves Moreau, a computational biologist at the Catholic University of Leuven in Belgium, who studies the ethics of human data.

It’s all even worse when you add artificial intelligence (AI) into the equation. AI enhances imaging capabilities and allows the surveillance technologies to recognize humans and their behavior through, for example, face or gait.

The analysis assessed 19,000 computer-vision papers published between 1990 and 2020 at the leading Conference on Computer Vision and Pattern Recognition, as well as 23,000 patents that cited them.

The researchers looked in depth at a random sample of 100 papers and 100 patents and found that 90% of the studies and 86% of the patents that cited those papers involved data relating to imaging humans and their spaces. Just 1% of the papers and 1% of the patents were designed to extract only non-human data.

Quite predictably, police forces and governments say that AI-powered surveillance allows them to better protect the public. But critics say that the systems are prone to error, disproportionately affect minority populations, and could be used to suppress protest.

And the trend has increased. In a wider analysis, the researchers searched all the patents for a list of keywords linked to surveillance, such as “iris,” “criminal,” and “facial recognition.”

They found that in the 2010s, 78% of computer-vision papers that led to patents produced ones related to surveillance, compared with 53% in the 1990s.

Almost “the entire field is working on faces and gaits, on detecting people in images, and nobody seems to be saying, ‘Wait, what are we doing here?’” said Moreau.

The researchers challenge the notion that only a few rogue entities enable surveillance. Rather, they found that the normalization of targeting humans permeates the field, and that this normalization is especially striking given patterns of obfuscation.

Within the industry, the field of computer-vision is usually portrayed as a scientific, data-driven endeavor designing new proteins, creating art, and modelling climate change.

“Obfuscating language allows documents to avoid direct mention of targeting humans, for example, by normalizing the referring to of humans as ‘objects’ to be studied without special consideration. Our results indicate the extensive ties between computer-vision research and surveillance,” says the study.

Moreover, within the industry, the field of computer-vision is usually portrayed as a scientific, data-driven endeavor designing new proteins, creating art, and modelling climate change.

“Yet, prominent computer-vision tasks, such as facial recognition, continue to be tightly tied to military and carceral use, heavily shaping core aspects and uses of these subfields,” the study points out.