You're going to need a lot of film. Good luck picking the controls for that that ensure your ML system picks up on subtle differences in humans that are lying and not glaringly obvious differences in appearance, accent and even background noise between the footage of the convicts and the control group...
I assume you would isolate the people in pre-processing, and do feature extraction of things you might think are interested in. Yes, it'd still require a lot of data, but that data may exist somewhere. A lot of these things are filmed.
From CIA, and TSA, to your local police department, everybody would have jumped all over it.
This is some vendor managing to sell some BS to bureaucrats (probably with the required bribes).
If CIA had it, you wouldn't hear about it. I don't see any reason why these countries couldn't pilot it for their border security before TSA, though.
FWIW, polygraphs are still used for security clearance screening. So, they are at least accurate enough for that use. They get a bad rep because they aren't admissible in court, but it's not like they have zero correlation to truth telling. They are still quite accurate.
Usually when people say 'AI', IBM included, what they mean is fairly trivial machine learning. Machine learning requires a dataset to learn on.
> In this case, there's excellent reason to be sceptical about whether the service has a remotely adequate dataset, because even if lie detection were a simple problem, how on earth are you going to get calibration data involving hundreds of data points of real airline passengers lying about having weapons in their luggage or intending to overstay their visa?
You could do it by watching film of interactions with border agents when it is still unknown whether or not the person was lying, and then use whether or not they were caught smuggling (or whatever) ex-post as your ground truth.