For a real world version of your "Bob" example, many people have negative associations with the names "Adolf" and "Hitler".
Do you think we should train AI to systematically ignore those sentiments?
Do you think we should train AI to systematically ignore those sentiments?
Do you think we should train AI to systematically ignore those sentiments?
For example, people from X race might be more likely to commit crimes in the absence of any other information (marginal probability). However, person from X race is no more likely to commit crimes than a person from Y race, conditioned on something else like where they went to school, what they do for a living, etc..
It's important to remember that AI doesn't have context, and just because it's using "data" to make decisions doesn't mean the decisions are unbiased - the underlying data may be biased.
You're not allowed to discriminate on race in housing. But is an AI that determines your creditworthiness for mortgages allowed to discriminate on what you eat, where you go to church, what your favorite music is, etc.? Maybe it doesn't have that data, but it will have one level higher - what store credit cards you have and how much you use them and where you opened them.
If you train an AI on a segregated city with a history of actively discriminatory citizens, where the few people of race X who moved into a not-race-X neighborhood got harassed out and sold before they paid off their mortgages, how easily will the AI conclude that people born in certain neighborhoods are more likely to pay off their mortgage if they avoid certain other neighborhoods?
Is that illegal? (My guess is there's no way to prove to a court, to the court's usual standards, that the AI happened to learn the city's racial tensions.) Should it be illegal, if outright racial discrimination is illegal?
This seems to be the case in London, too, so it's a bigger issue than just the US.
But for things like drug possession, yes, blacks probably get stopped much more and not let off with warnings much less.
It is like with plagues. If you put many people with plague in one area do expect that others will catch it. If the rule to segregate is silly enough do expect a correlation of plague with certain characteristics of people put together and the locations. Or their sizes. Maybe it is "cities" or "presence of slums" not skin colour, combined with overrepresentation of people with certain skin colour in them.
It takes some write genius analysis and experimentation to untangle such complex effects from causes.
Call again when you have an AI that can deal with this. Essentially a researcher AI.
If lack of money causes homicides, we should expect to see all poor rural areas have high homicide rates, too. Maybe that's true.
Another factor seems to be sex. Males commit so much more violence, so perhaps that's the only thing to focus on.
Much as there is much hype about AI and machine learning technologies, all of such systems will be, for the foreseeable future, very simplistic models of what we think we know of intelligence.
As humans, we have blindly forgotten that we know very little about the world around us, including ourselves. We think that we have a handle on reality, but we are just plain ignorant. All the models that we have for deep learning and AI or GAI are barely scratching the surface of what "intelligence" is and means.
We may get some useful tools as a result of the research being undertaken today and what we have undertaken over the last lot of decades. But we have millenia to go before we even scratch the surface of what we understand of the universe around us, let alone understand what intelligence means.
That is reasoning. I’ll agree with your later point that we don’t really know what intelligence is yet, but that’s because reasoning is clearly only one type of intelligence. Current systems are terrible at language, for example (a year ago I would have said “and spatial awareness”, but this is progressing fast and I am no longer sure).
> They cannot recognise in any way that the rules by which they operate or the data they are supplied with are or are not reasonable.
Quite a lot of humans fit that description. For example, consider how much angry disagreement the following questions get: “is climate change real?”, “Brexit, yes or no?”, and “does god exist?” Also consider how many people (angrily!) refuse to believe these questions result in any angry arguments.
But your comparison is inapt: yes, we should train AI to systematically ignore negative sentiments it associates with people named "Adolf" or "Hitler" who are not in fact the Adolf Hitler who died in Berlin in 1945. Humans have difficulty in doing so, of course, but this is a bug in human cognition which is essentially the vulnerability that bigotry takes hold of: a justifiable negative impression of one person as an individual is imputed to other people who seem superficially similar. We see one person of a minority breach a social norm or even a law, and we think that others of that minority must be prone to doing similar, simply because their minority status is salient. We fail to realize subconsciously that membership in the same minority is not actually meaningfully correlated with this behavior, and when we see someone from the majority do the exact same thing, their majority status is less salient, and we don't impute the negative impression to the majority.
A good AI should be able to distinguish dictator Adolf Hitler from, say, saxophone inventor Adolph Sax or chemist William Patrick Hitler (the nephew of Adolf Hitler), and not cast aspersions on the latter two - even though human biases forced William Patrick to change his last name to Stuart-Houston. It should even be able to understand that Indian politician Adolf Lu Hitler Marak is a separate person who merely had parents with questionable taste, and the man is not on account of his name more likely to become a genocidal dictator than any of his political rivals.
And since our justifiable negative association with the Nazi leader is, fundamentally, that he weaponized this vulnerability in human cognition, it is one way of acting on our dislike for this Hitler to make sure that the AIs we build are not susceptible to the same vulnerability.
Isn't that statement in itself an invocation of Godwin's Law?
Godwin himself wrote a little bit about it: https://web.archive.org/web/20170209163428/https://www.washi...
(And I am totally open to criticism that this particular comparison is inapt.)
You can ascribe it to "Godwin's law" as much as you like, I just find it a more realistic example than some hypothetically disadvantaged "Bob".
We should make sure that an AI, who is probably making decisions on things like legal documents / public records and not just the middle name someone goes by, will not consider it a negative that someone is named "Adolf" if they aren't Adolf Hitler specifically. And we should for the same reason make sure that an AI will not consider it a negative that someone is named "Bob" if it has a newly-acquired specific negative impression of some other person named Bob. There isn't a difference in the cases.
"Hitler" shouldn't be a special case because the rise of Hitler wasn't as much of a one-time event as we'd like to believe. When the next genocidal dictator with a somewhat rare first name gains control of a country, there will be people from the victim population who share that first name, and they should not suffer the same indignity at the hands of an AI, either. And on the flip side, when this genocidal dictator rises to power, the AI shouldn't be taught that Hitler was the only evil man who ever lived or will live; if it has the data to conclude that some actual individual (not a name) is as bad as Hitler, it should be able to conclude that.
If you are referring to the actual well-known Adolf Hitler, then there should be no need to use the name for the purposes of making decisions. You shouldn't need to use a person's name as a proxy for whether or not they are a genocidal dictator, just make the decision based on whether or not they actually are a genocidal dictator.