The ethical questions that haunt facial-recognition research

One Chinese American AI researcher who didn’t want to be named said that a problem was a cultural split in the field. “The number of Chinese researchers at top conferences who actively support censorship and Xinjiang concentration camp[s] concerns me greatly. These groups have minimal contact with uncensored media and tend to avoid contact with those who don’t speak Mandarin, especially about social issues like this. I believe we need to find ways to actively engage with this community,” they wrote.

Nature asked researchers what the scientific community should do about ethically questionable studies. The most popular answer was that during peer review, authors of facial-recognition papers should be asked explicitly about the ethics of their studies. The survey also asked whether research that uses facial-recognition software should require prior approval from ethics bodies, such as IRBs, that consider research with human subjects. Almost half felt it should, and another quarter said it depended on the research.

Ethical reflection

Researchers who work on technology that recognizes or analyses faces point out that it has many uses, such as to find lost children, track criminals, access smartphones and cash machines more conveniently, help robots to interact with humans by recognizing their identities and emotions and, in some medical studies, to help diagnose or remotely track consenting participants. “There are a number of lawful and legitimate applications of face and biometric recognition which we need in our society,” says Jain.

But researchers must also recognize that a technology that can remotely identify or classify people without their knowledge is fundamentally dangerous — and should try to resist it being used to control or criminalize people, say some scientists. “The AI community suffers from not seeing how its work fits into a long history of science being used to legitimize violence against marginalized people, and to stratify and separate people,” says Chelsea Barabas, who studies algorithmic decision-making at MIT and helped to form the CCT this year. “If you design a facial-recognition algorithm for medical research without thinking about how it could be used by law enforcement, for instance, you’re being negligent,” she says.

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