I recognize that this is not immediately an empirical claim, but I will observe that discarding it offhand in favor of the equally ideological dominant interpretation would be exactly the dynamic I'm pointing out.
If every doctor trains an ML algo, then forgets how to be a doctor, then medicine stops dead at that level.
This seems like its happening at some hedge funds as well for stock picking.
I guess the closed system of the game rules is more manageable than all of medicine, but... I can't agree with "stuck as the current level of expertise".
It's very rare that technology becomes ubiquitous and free very quickly. You will still need doctors in poor areas, destitute countries, and in the military.
Still, education and society would likely optimize for getting people to the edge of knowledge more quickly if ML algos truly took over a field like medicine.
(see the MIT moral machine)
I'm not sure it's dystopian. Maybe human decision making will be considered art. That's an awesome thing, but not necessary what you do in workplace.
For instance, let's say you pull up to a red light and the intesection is empty. No cars, no pedestrians.
Common sense tells you to just run the light, there is no reason not to.
But it's illegal for a human to use their own judgement to do this, and the instructions of the red light have to be followed regardless.
No they don't. If there's no enforcement, and no moral reason to follow the law, you've reduced yourself to a robot if you follow it.
As far as red lights are concerned though, you'd better be damn sure cars or bikes or pedestrians aren't going to leap out from around a corner before you run them. Especially if the intersection is empty at 3am and visibility is poor. In fact in that situation it's better to play it safe.
By can, I mean that a human being can choose to do so. Maybe smart cars in the future won't allow the driver to make that decision. But nothing forces us to always obey traffic laws right now.
Much domain knowledge is in some sense innate or even subconscious, picked up in the field. I think it would be more efficient just to spy on people’s thoughts (and work) in some manner if there is a fear they could misapply their knowledge or leave a critical process hanging.
The reasoning will go, yes we are losing $1 on every automated transaction, but we can make more of them, and we certainly avert one $10,000 corruption case and one $100,000 case where we get sued for human decision.
You can't sue machine learning algorighm (that how it would go). But human, you can, even frivolously.
(I am one of the researchers mentioned in the article.)
"Joe, why did you fire Carla?"
"She was late to work every other day and spent all day on her phone"
"HR-bot 5000, why did you fire Carla?"
"Weights indices 59738, 837, and 28836 added to over 0.67, yielding a score of only .85, which is below the firing threshold"
Certainly you could look at the input data and try to convince a jury that her performance wasn't acceptable but you can't prove that is why the AI fired her.
The reason developers got fired is because of clear and well-defined rules... that were never enforced except when the decision to fire them for an arbitrary reason had already been made.
I think HR-bot 5000 would treat employees more equitably by forcing employers to confront the inequity of some HR regulations. The same way self-driving cars "drive weird" because they stop at stop signs and drive the speed limit.
There will be protocols for sensitive areas.
Can't do the same with individual decision making.
The problem is that the end weight distribution is different than the GB's or TB's of training data. How do we know the training was fair and impartial? How did the trainers even know if it was? What biases crept in on this stage?
Worse yet, what if the bias of the black box does denigrate black people... Say, we take in all pictures of convicted criminals- it's disproportionaly black. I would argue part of that is because of inherent policing biases, but that's embedded in "guilty" verdict. Who's at fault for this "bias"? Is there a fault? How do we detect, other than exhaustively?
I'm eagerly awaiting for methods to "open up" ML black boxes and see what makes them tick. See their decision trees, their neural weights. I want to poke and prod to see what's behind those series of numbers like [.888271829 1.10999292992 37.999999921 1000.32 .73] . Right now, it's shove data in exhaustively and hope for the best. I don't particularly care for that way of analysis.
If you have learned to do something, you will proceed by teaching a machine learning system that. And then you will use that, and not your internal knowledge.
The reason will be cited as follows: If you know something, this introduces bus factor. People can't get in your head to validate if you're applying your knowledge fairly, or biased, or maliciously misappropriating things. Your reasoning is unverifiable. You can leave for greener pastures, carrying your skills and knowledge with you.
While machine learning system is repeatable, you can have all kinds of checks and KPIs. And it's protected by IP laws.
Human domain knowledge will be discouraged.