That's probably because the limbic system accepts thriving and surviving as an unquestioned goal. Anything that promotes these, to the limbic system, is a categorical imperative.

Our moral systems probably got more refined as society grew, but by our very nature as live beings we need to have an understanding between right and wrong to inform our actions. A computer doesn't have this understanding, it just follows the instructions its given, always.

I'm not making the argument that machines are incapable of empathy, although I am by extension, but the core of the argument is that machines are incapable of experience. Sure, you could train a computer to spit out a socially acceptable moral answer, but there would be nothing making that answer inherently moral to the computer.

I agree that little children are often psychopaths, but they're not incapable of experience. They have likes, dislikes. A computer does not care about anything, it just does as it's told.

The fundamental difference between a human hunch and the odd correlation the AI makes is that the correlation does not mean anything to the computer, it's just moving data like it was built to do. It's a machine.

Can't help but think of Peter Griffin's "Oh my god who the hell cares" with stuff like this.

I don't necessarily agree that we need to have what you call 'unexplainable AI' and what I would call 'AI using machine learning' to solve the kinds of problems that face police today. I think that you can have systems that are extremely unbiased and extremely transparent that are written in ways that are very explicit and can be understood by pretty much everyone.

But I do agree with you that it's a very biased and incomplete argument to say that automated systems are working in ways that are opaque to the communities they serve and ignore the fact that it's not in any way better to have humans making those completely opaque decisions.

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In my experience children tend to be little psychopaths. Right and wrong (morality) likely evolved along with humans as they developed societies. Societies give a significant boost to the survival and propagation of members within the society. So societies with moral systems that are conducive to larger and more efficient societies tend to propagate better as well. These moral systems then get passed on as the society propagates and any society that has morals not conducive to societies tend to die off.

Why do you believe an AI would definitely be incapable of empathy? Not all humans are even capable of empathy and empathy can even be lost by damage to the frontal lobe. For some of those that have lost it never returns and for others they are able to relearn to express it. If it was relearned does it mean they are just emulating it and not actually experiencing it? How would that be different than an AI?

When humans get intuition, a feeling, or a hunch it isn’t out of nowhere, they typically have some kind of history or experience with the subject. For example when a detective has a hunch about a suspect lying it could be from previous experience or even a bias from a correlation with behavior of previous lying subjects that other detectives haven’t really noticed. How fundamentally is this any different when an AI makes an odd correlation between data using statistics? You could argue that what an AI is doing when correlating data like this it is creating a hunch and when a human has a hunch they are just making a conclusion using correlated data.

Note I am not advocating using AI in policing, I believe that is a terrible idea that can and will be very easily abused.

China has entered the chat.

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Tbh, I don't think most of the people even vaguely understand the difference but are thrilled at the opportunity to morally grandstand against a supposed injustice.

The thing is, for a baby to be hungry, it needs to have some sort of concept as hunger being bad. We need the difference between good and bad to stay alive. A computer doesn't, because it doesn't need to stay alive, it just runs and shuts down according to the instructions its given.

We need to learn ethics, yes, but we don't need to learn morals. And ethics really is the study of moral frameworks.

It's not because the computer is not advanced enough. It's because the computer is a machine, a tool. It's not alive. It's very nature is fundamentally different from that of a live being. It's designed to fulfil a purpose, and that's all it will ever do, without a choice in the matter. It simply is not "in touch" with the world in the way a live being is.

It's natural to empathize with computers because they simulate mental function. I've known people to empathize with a rock they named and drew a face on, it doesn't take that much for us to become emotionally attached. If we can do it with a rock, we stand virtually no chance against a computer that "talks" to us and can simulate understanding or even respond to emotional cues. I would argue that it's far more important we don't lose sight of what computers really are.

And if someone were to design a computer capable of suffering, or in other words, a machine that can experience - I don't think its possible and it would need to be so entirely different from the computers we know that we wouldn't call it a "computer" - that person is evil.

As I've pointed our elsewhere in the thread, I think a lot of people aren't distinguishing between an explicit algorithm, and a machine learning algorithm. I think people in this thread are looking at algorithms as a black box, where you put data in, something incomprehensible happens, and then police go and arrest people. When you have machine learning, it's a non-deterministic process where even the programmer who built the system can't work it backward and say 'this person was arrested because of these inputs to the system.' But there are tons of algorithms that could be developed where the programmer can tell you EXACTLY which inputs lead to a particular result, and the transparency of these algorithms could vastly exceed the transparency of machine learning, and even exceed the transparency of our current human-driven system.

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I feel like Superiority theory, and Relief theory are more or less subsets of Incongruity theory. This doesn't help the idea that incongruity is unfalsifiable, but having a theory that perfectly describes all humour would be bounded only by the non-humourous, so I'm not sure what to think there.

In the case of Superiority, perhaps the thought that this person could be superior to you is incongruous to you. Personally, I find very little humour in this category, and might choose to call it scorn instead, where laughter might be an aspect of glee or amusement rather than comedic. This certainly makes me wonder how this article/author might define the term humour.

As for Relief, it seems that the emotions aren't quite built up as expected. Expectations being subverted might involve a tonal shift, rather than pressure; a relatively serious setting leads one to expect serious discourse, so something comical disrupts that, more like a pressure change.

It's interesting how this article only briefly eludes to the function of humour in society, as I think that is the place to look for a falsifiable and scientific theory of humour.

And you're missing the point of the field if you're making the trivial observation that working out an explanation decreases the usefulness.

That is the point. We want to decrease it's usefulness and increase its accuracy in fields where the accuracy is paramount. This is akin to the relationship of physics and math. In physics, we routinely make unjustified steps to make our models work. Then in math, we try to find a reasonable framework in which the unjustified steps are justified. Saying "math reduces the usefulness by requiring an explanation for seemingly okay steps" is to miss the point of what mathematics is trying to do.

100%, spot on.

People are acting like this AI would only speculate off that past history and not constantly update the model.

You could literally feed in historical data that says there's only crime in neighborhood A despite the opposite being true and the AI would correct the issue within a few cycles as you said. The big thing here is these prediction models learn and they only learn off of input. If everything but the location & type of crime was scrubbed from the data, literally no demographic information at all, the results would come out the same.

I think even philosophically we're at a point where we can't even discuss that the data might just be data without people crying foul and it disgusts me. Racism by low expectations is still racism. I grew up in a very, very shitty neighborhood B. I've also lived in Neighborhood As. I can't say A was completely without incident, but comparing the two even off of my anecdotal experiences is night and Day.

I think the biggest incident in A was someone complaining about Horse droppings on the beach and some teens setting a dumpster on fire.

B had someone get shot. Completely anecdotal but still relevant.

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A baby doesn't need to learn to be hungry but neither does a computer need to learn to do math. A baby does need to learn ethics, though, and so does a computer.

Whether or not a computer has something fundamentally missing that will make it never able to have a notion of "feeling" as humans do is unclear to me. You might be right. But maybe we just haven't gotten good enough at making computers. Just like we, in the past, made declarations about the inabilities of computer that were later proved false, maybe this is another one?

It's important that we are able to recognize when the computer becomes able to suffer for ethical reasons. If we assume that a computer cannot suffer, do we risk overlooking actual suffering?

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But if you were training such a model you would obviously want to include in its training data how much time police were spending there already, so it ought to be able to distinguish between an area where there are more arrests because there is more crime from one where there are more arrests because there is more police.

Not really, because you can write the algorithm to have as long (or short) a memory as you want it to have. You could even write an algorithm that gives zero weight to all historical crime data and starts by assigning officers randomly throughout the community, and then it continuously updates that distribution of officers based on the crime data starting only from that randomized initial condition. It's basically just wrong to argue that you have to start with an objective data set, you can start with absolute garbage data and the only effect might be that it takes your algorithm a few extra cycles to get past that and converge on a sensible state.

I don't think the OP failed to understand the technology of algorithms at all, and I've been an embedded systems engineer and programmer for 15 years. I think the OP was absolutely right in pointing out that what we're afraid of is that the systems will end up with coverage maps that look too familiar to us, and we won't want to confront that reality. I don't know if that's the case, but I think it's accurate that it's what people fear is the case.

>statistically, out of 1000 conspiracy theories, it is simply impossible for all 1000 of them to be totally wrong.

what specific statistics support that view? because a person could make up millions of imaginary scenarios and none of them might be true.

I didn't mean that the AI should be able to explain itself. I meant that we should be able to dig in to the AI and find an explanation for how it worked.

I'm saying that that requiring either would limit AI and decrease it's usefulness.

Already we have models where it's too difficult to dig into them and figure out why a choice was made. As in, you can step through the math of a deep learning system to follow along with the math but you can't pinpoint the decision in there and more than you can root around in someone's brain to find the neuron responsible for a behavior.

>Data being collected on facial expressions in the billions is more likely. Then you correlate that with other stuff. Bottom line, it's as if the cameras are installed in the privacy of your home, because mountains of data in public provides the missing data in private.

I would say this constitutes "monitoring everything out of the neurotic obsession someone might do something that's not allowed", wouldn't you?