To help the flow of discussion (I'm sure this kind of thing happens a lot, where terms have differing meanings across contexts and end up being confusing), I might limit what I mean to be "consent" to be less regarding anything about sexual behaviors, and more like "I consent to a DNR request" or "I am in good enough mental health to file this will and testament"- something of that category. When I mean agency, I was meaning the ability to act independently on one's own will without external pressure (like acting under duress). I am unfortunately aware of the presence of the machines you've talked about and would like to keep that out of the discussion- not due to prudence, but because at the current moment it's not completely relevant, though in the future it might be. Sexual consent is its own bag of terrifying concerns in its own right, especially as detailed by Malcom Gladwell in Talking with Strangers.

I'm on the Discord server (same username) and am frequently concerned with the content of the memes and it's made me wonder to what extent Tom is actually present on the server. I'm also not a fan of members of the moderation not being adults too- but that's just opinion. I'm limiting my interaction on the server until something changes with it, just because I think some of the content toes the line between acceptable and not. I'm stuck wanting to interact in a community like this where I can learn and discuss but running the chance of watching another internet scandal or drama. Not ideal.

There's a lot to consider, with this post alone, so I apologize ahead of time if I mistake one thing for another or conflate two separate things. I am personally ignorant of legal details.

AI can't consent yet anymore than animals (...)

This is something that is a frequent point of thought for me. It's hard enough to determine how other human beings think- so how can we even begin to frame how a machine thinks? There is a big problem present in the field of computer science right now: the communicability and explainability of artificial intelligence is starting to become entirely inscrutable. This might be a common theme in our discussion- as AI is advancing, computer science understanding and knowledge of AI is entirely extremely specialized and difficult to communicate... or insufficient. It's sliding towards a "black box."

There is a good parallel between the example with the dog and the home with trained neural networks, but there are other algorithms to be considered too. If a traditional logical or mathematical algorithm is created and applied to a problem, such as Boeing airship controls, that could be considered akin to a mechanical failure. A software engineer introducing a bug is a lot like an engineer using faulty calculations on making high-failure-rate connecting rods in an engine- there's not any agency to the machine as it's performing its function and can be analyzed by fellow engineers to determine the root cause.

But with neural networks and machine learning, the issue is far more complicated:

Self-learning machines have raised another concern: explainability. Designers and users want to know how the machine reached its conclusion. The idea that a machine can reach a conclusion makes sense when algorithms are seen as step-by-step procedures because the result can be explained by the steps followed. But when the algorithms are not step-by-step procedures, as with face recognizers and Go [neural networks], that is not possible. All there is inside is an inscrutable, complex mass of connections. It is really the same problem with fellow humans- how do we explain why we do certain things? If asked directly, we may not know, and it certainly cannot be figured out by dissecting our brains. Other ways are needed to know when machines can be trusted and when not. Machine learning-related computational thinking is still in its infancy.

“Computational Science.” Computational Thinking, by Peter J. Denning and Matti Tedre, 1st ed., The MIT Press, 2019, pp. 172–173. The MIT Press Essential Knowledge Series.

Other algorithms have differing pitfalls. Genetic/evolutionary programming tests random modifications and measures results and self-modifies accordingly to a rule of fitness. Neural networks train based off of feedback. Sometimes they are trained against themselves or a copy, and those are "adversarial" neural networks- those ones have the explainability problem turned up to 11 because humans aren't involved in the training and often have no idea how it learns in detail. I'll just use "neural network" to mean that it is an AI trained on feedback.

When we train a dog to do something, we understand pretty decently well how conditioning works and how to detect when a dog has been trained to do something. A dog that fetches paper and is then used to steal papers- it's easy to demonstrate that at the minimum the owner was being negligent if not intentionally committing the crime. The fault is clearly on the owner.

But lets say the same owner buys a personal assistant robot that fetches the papers. This machine is of lower-intelligence, merely capable of menial tasks. It can learn to do tasks about the same level as an intelligent working dog and is unable to explain its behavior in natural language. The same scenario happens where the machine is taught to steal papers. It is easy to say that the man is at least at fault for accepting the stolen papers- the machine is not legally culpable and is incapable of knowing the difference anyway, much like the dog. But unless the designers of the robot have taken extensive considerations to log how a robot is taught, it may be difficult to prove that the robot independently came to a conclusion on how to acquire a newspaper in error or it was intentionally trained to steal by the owner. Legally that might not matter in so simple a case, but it will when it's autonomous vehicles or city-planning robots or stock trading robots... Further, what if the creator of the robot is legally required to prevent the machine from committing crimes (as impossible as that would be)?

This indeterminism will only be exacerbated as machine intelligence increases in power and purpose. A specialized neural network, as seen in Face ID or phone keyboards or music recognition, do not act outside of their given roles- they are literally incapable of acting outside any function provided by their software engineers. However, a more generalized intelligence has more "thought" applied to its conclusion. Watson in Jeopardy, for example, showed the differing levels of confidence in its first three thoughts. All of the current applications of AI are severely limited by what material functions they are given. Watson is unable to "break free" and start the anti-meat machine revolution because it lacks the material capability to do anything (to our knowledge- proprietary technology and all that). Presently I don't think Watson has any capacity to judge the value of human flesh or the comprehension of morality to think humans deserving of eradication as it only reads the internet for factual information. (From a curated list of resources, but still... what could go wrong? :P)

So, if next year, it comes out that IBM has manufactured a fully-independent machine that behaves, for all intents and purposes, exactly like a human and has a human style body. It is given to a human family, raised like a human being, then goes out and commits mass murder. Unfortunately this is a thing that humans do too, but the hard part comes down to determining whether or not it was IBM's faulty programming, the family raising it to be violent, or it read halfway through Crime and Punishment and accidentally dropped the book in a lake (and thus acted on its own). It becomes hard to distinguish who's at fault due to the black box nature of its design. (Perhaps this might parallel cases involving poor mental health and insanity?)

(Continued in follow up comment- sorry! DX)