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This misses the entire point of what explainable AI is. Asking humans to explain their intuition as a precondition for their intuition to be applicably valid is definitely limiting for humans. However, explainable AI isn't that we ask AI to explain itself. It's rather being able to exactly or with high probability pinpoint the exact dataset on which AI is basing it's prediction. This is definitely useless, and so limiting, when it comes to machine learning applications to, say, predicting what food you might like the best. It's however immensely important in areas like medical imaging, because we want to ensure that the input, on which AI is basing its decision, isn't some human-errored spot on the x-ray.

As such, it is for these fields that explainable AI is studied, where limitations of AI are far less significant than us being sure that AI isn't making a mistake. As such suggesting explainable AI is a dead-end is inaccurate, if not a mischaracterisation.

Assuming data itself is biased is the heart of this issue and why people shouldn't be allowed to handle it at all.

Claiming "that era was racist" so all data must be discarded is a cop out and ignores the issues.

Data is nothing but points. Acting like Middle class, Median income A and Lower class, low income B will have similar or equal crime rates is insanity and racism. Pretending like A has the same amount of crime, they're just not patrolled is ignorant at best, racist at worst.

If i had to guess, #3 applies to most things. Id add in "irony" as a more generalized #3.

The fact that we're being tortured to death slowly, all of our ancestors watching our every lay, for everything to end with zero consequence gives a sense of irony. Heh...

That might happen and it's a danger but that's not the mainline scenario.

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.

Then you correlate the inferred private stuff with more stuff. That's how you build "Minority Report"

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>Neighborhood A. Neighborhood B.
>
>A has minimal police patrols, minimal police calls, minimal interactions with law enforcement.
>
>B has regular patrols, regular calls and frequent interactions with law enforcement.

correction: if you are using algorithms all you can say is "Neighborhood A had minimal police patrols..." because you are always looking into the past.

in the past there were no algorithms. so you start the historical data set where? in the 1940's? 50's? 60's? those were racist days. so were the 80's, 90's and 2000's.

if you don't start with an objective data set then your algorithms will be biased. and with backward-looking algorithms you won't know that a neighborhood profile has changed until its recorded stats are significantly different. in the meantime you'll be letting crimes go unaddressed.

your particularly unsophisticated approach to a very sophisticated technology (which you fail to understand) is at the heart of this issue.

I remember reading something that was comparing different forms of control in society. I think it used Western media controlling narratives as an example of more subliminal control, and then compared to something like the subjective enforcement of hard-coded rules in potentially the USSR. I want to say it may have been Zizek as I think there was some humor to the comparison pointing out the irony.

I was wondering if this rings a bell for anyone because I am having trouble finding it.

Algorithmic thinking isn’t restricted to computers. Bureaucracatic humans can fall into the same pitfalls as machines. I’m fond of saying, the thing we ought to fear is not computers that are becoming more human, but humanity becoming more machine-like

That's good enough (not making hasty assumptions)

Does a baby need to be taught to feel hungry?

While I appreciate the comparison you're making, it poses a massive problem: who initially taught humans the difference between right and wrong?

Kids do good without being told to. They can know something is wrong without being taught it is. For a computer, this simply is not possible. We're not teaching kids what "good" and "bad" are, as concepts. We're teaching them to behave in accordance with the morals of society at large. And sure, you could probably teach a computer to simulate this behavior and make it look like it's doing the same thing, but at the very core, there would be something fundamental missing.

What's good and bad isn't a purely intellectual question. It's deeply tied in to what we feel, and that's what a computer simply cannot do. Even if we learn it to emulate empathy, it will never truly have the capacity to place itself in someone's shoes. It won't be able to even place itself in it's own shoes. For as far as it's trying to stay alive, it's only because it's following the instruction to do so. A computer is not situated in the world in the same way live beings are.

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No it’s false. There is no such thing as something that is neither true nor false.

It's neither, you can't divide by zero because the quotient is unknown.

The value exist but cannot be determined.

"The value does not exist" is more an hand waive to make the issue easier to understand.

Which is making my point that truth can be more granular than anything else because what you said is not incorrect but incomplete.

You can read about it here:

https://en.wikipedia.org/wiki/Division_by_zero

Are people so different? We spend years teaching our kids to know right from wrong. Maybe if we spent as much time on the computers then they could know it, too?

This assumes the prior data was done without bias firstly. If they are currently overfocusing on one area due to some bias the algorithm will have that baked in due to the data it is given to work with. Secondly, that seems like it would be prone to a feedback loop. More police focus could itself be a reason for more incidents. As was pointed out in the article, similar crimes in a strongly policed area would be more likely to be caught. This would increase numbers in that area and make it look like that area needs more attention, not because there is more crime but because there is more crime already noticed.

I'm not saying so much as I am demonstrating how a person behaves when a point is raised that conflicts with their presentation of reality. In this case, you dodged the question - in my experience there are < 10 standard behaviors, and this is one of them.

Machine Learning, Artificial Intelligence, and Algorithm are all terms that exist in the same space of computer science, but they absolutely do NOT all mean the same thing, and in your post here you used them all interchangeably.

An algorithm is a very generic term for some kind of heuristic that can be followed to produce some result. A recipe for cookies in an algorithm just like some algorithm on Facebook decides what posts to show you. Machine Learning takes place when the process a system implements is non-deterministic; it does things that the programmers didn't explicitly tell it to do; it actually learns how to do new things. An artificial intelligence is a system that's designed to do tasks in the same way a human would, often involving processing visual data or making human-like decisions.

If you wanted to make the case that we shouldn't use MACHINE LEARNING in policing, I would 100% agree with that statement, our police policies should be very deliberate and very transparent and machine learning wouldn't be either of those things. But using this as an argument that we shouldn't be embracing policing with explicitly defined algorithms that are far MORE transparent and deliberate than the humans they would replace is an absolutely indefensible argument. If there's one thing we've learned in the past few years, it's that police need far more regulation, and that's exactly what algorithms do whether they are implemented by a computer or by some system of rules and laws.

If we are searching for something that does cannot be evidenced to exist, then it is not possible to say that x.....y are not true.

If I pull a concept out of my ass and say that nothing is this x, it is different thing to say as we should that the whole concept does not exist rather than trying to catch that false question setting like dogs. Because of course saying that nothing is x is not innocent but a way of doing something, implying obligation etc. For example the justification of capitalism is that no morals can be measured so we should welcome laissez faire.

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Idk what you want to say with that. There is no bomb or if there is may be you should call the police. Im only saying that there may be a procedure for something but outcomes are no longer possible. One could possibly know how to catch butterflies but he/she could actually catch them any longer if they had become extinct.

I'm not talking about reasoned explanations when I say a computer does not understand what it's doing. What I mean is that a computer fundamentally has no concept of "right and wrong." It's just a field of data and to the computer it's all the same if you switched the field for "good" with the field for "bad," it would uncaringly keep making it's calculations. Computers do not feel, they do not have hunches. All it does it measure likeliness based on ever more convoluted mathematical models. Its a calculator.

Any emotional attachment is purely coming from our side. A computer simply does not care. Not about itself, not about doing a good job, and not about you. And even if you told it to care, that would be no more than just another instruction to be carried out.

As an example:

Meta just a couple days ago took offline it's scientific paper generation machine because it would happily provide you a real-sounding scientific paper on the history of bears in space.

https://futurism.com/the-byte/facebook-takes-down-galactica-ai

> The computer has no idea what it's actually doing

Counterpoint: Neither do we.

Expert poker players are often unable to explain their reasoning for why it felt like a bluff. It could be that they are picking up on something and acting without being able to reason about it.

Likewise, a doctor with a lot of experience might have some hunch that turns out to be true. The hunch was actually solid deduction that the doctor was unable to reason about.

Even you, driving, probably sometimes get a hunch that a car might change lanes or get a hunch that an intersection should be approached slowly.

I (and others) feel that explainable AI might be a dead-end. If we told the poker player that you can only assume a bluff if you can put into words what is wrong, that player might perform worse. It might be that forcing AI to be explainable is artificial limiting it's ability to help us.

Even if you don't buy that, there are those studies that show that consciousness is explaining our actions after the fact like an observer. So we're not really using reason to make decisions, we just do things and then reason about why.

We let humans drive cars on hunches. Why should we hold AI to a higher standard? Is a poorly performing explainable AI better than an unexplainable one that does a good job?

Also called racial profiling!