>Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. Delegation of learning has clear benefits, and at the same time raises serious concerns of trust.
My understanding was that the threat model for data poisoning is when the attacker controls part or all of your dataset, not the learning algorithm. Am I getting this wrong?
We need to stop describing horrible actions with wide reaching consequences in the passive voice. And we need to start socially punishing people who insist on doing so. Otherwise the wheels are coming off.
Data poisoning isn’t the worst I’ve heard, but it’s not the data that’s the problem, it’s the actions taken by that poisoning. That’s the subversion that matters, not “the data”.
In what way is data poisoning in the passive voice? It's a nominal group. Pretty efficient and straightforward. Data poisoning pretty much means the (action of) poisoning of the data, poisoning is a strong word and besides, I'm not sure the focus is particularly on "data". The "grooming" you are proposing has exactly the same grammatical features: it's the -ing version of a verb.
Because it makes it sound like I broke one of your drinking glasses instead of killing your dog. Data is an inanimate object. Misusing data affects Organics.
Well by that token, grooming might as well refer to combing your hair. That's clearly daft, though.
In fact, its current usage probably first emerged in the 1970s in relation to child abuse [0]. Since then it's been hijacked by various right-leaning individuals and groups as a dog-whistle for whatever they happen to be most worried about today [1]. That makes it a heavily over-loaded word that's becoming a general fnord. The problem with fnords is that they discourage thinking. So I'm not in favour of using 'grooming' to refer to data/model poisoning.
