[mufasachan]

A simple research on Scholar with "facial recognition liars" leads to a few niche articles about the topic. So it seems that facial recognition for liars is a thing. After a quick look on the articles you can see those methods are very simple and they are not studied at the state of the arts level. It looks like much more an academic research field rather than an industrial field. So her claims are preventive alarmist.

Although, I would say she is not the most informed person in surveillance task in AI. Another surveillance field in AI is the "person re-identification" task. It aims to determine if two pictures of a person from surveillance cameras depict the same person or not. This is my field of research and you have a tons of papers each year in big AI venues and also big company in the author (alibaba, tencent, mostly big chinese tech companies, I saw one paper with Google once on a related task).

Also, I would like to highlight something. She said we need some people to annotate the datasets. For this surveillance task, actually, we have a bunch of methods that aims to less rely on human annotations : LUPerson and LUPerson-NL datasets and respective methods (CVPR 21 and 22) for example.

Finally, I would like to conclude that I mostly agree with her. A bit of nuance would be to see the interaction with the nature of data and the surveillance capabilities. AI on camera data are obviously surveillance. When it comes more blur it's what "surveillance" is for "code taken from the internet to train Github copilot method"? Or what is the surveillance capability from database of digital arts? Those questions seem to be much more relevant and hard in regard of the use of data for surveillance. AI is just a name of a method that we use to process big data at scale in this context.