Giving Algorithms a Sense of Uncertainty Could Make Them More Ethical (technologyreview.com)
An anonymous reader quotes a report from MIT Technology Review: Algorithms are increasingly being used to make ethical decisions. They are built to pursue a single mathematical goal, such as maximizing the number of soldiers' lives saved or minimizing the number of civilian deaths. When you start dealing with multiple, often competing, objectives or try to account for intangibles like "freedom" and "well-being," a satisfactory mathematical solution doesn't always exist. "We as humans want multiple incompatible things," says Peter Eckersley, the director of research for the Partnership on AI, who recently released a paper that explores this issue. "There are many high-stakes situations where it's actually inappropriate -- perhaps dangerous -- to program in a single objective function that tries to describe your ethics." These solutionless dilemmas aren't specific to algorithms. Ethicists have studied them for decades and refer to them as impossibility theorems. So when Eckersley first recognized their applications to artificial intelligence, he borrowed an idea directly from the field of ethics to propose a solution: what if we built uncertainty into our algorithms?
Eckersley puts forth two possible techniques to express this idea mathematically. He begins with the premise that algorithms are typically programmed with clear rules about human preferences. We'd have to tell it, for example, that we definitely prefer friendly soldiers over friendly civilians, and friendly civilians over enemy soldiers -- even if we weren't actually sure or didn't think that should always be the case. The algorithm's design leaves little room for uncertainty. The first technique, known as partial ordering, begins to introduce just the slightest bit of uncertainty. You could program the algorithm to prefer friendly soldiers over enemy soldiers and friendly civilians over enemy soldiers, but you wouldn't specify a preference between friendly soldiers and friendly civilians. In the second technique, known as uncertain ordering, you have several lists of absolute preferences, but each one has a probability attached to it. Three-quarters of the time you might prefer friendly soldiers over friendly civilians over enemy soldiers. A quarter of the time you might prefer friendly civilians over friendly soldiers over enemy soldiers. The algorithm could handle this uncertainty by computing multiple solutions and then giving humans a menu of options with their associated trade-offs, Eckersley says.
Eckersley puts forth two possible techniques to express this idea mathematically. He begins with the premise that algorithms are typically programmed with clear rules about human preferences. We'd have to tell it, for example, that we definitely prefer friendly soldiers over friendly civilians, and friendly civilians over enemy soldiers -- even if we weren't actually sure or didn't think that should always be the case. The algorithm's design leaves little room for uncertainty. The first technique, known as partial ordering, begins to introduce just the slightest bit of uncertainty. You could program the algorithm to prefer friendly soldiers over enemy soldiers and friendly civilians over enemy soldiers, but you wouldn't specify a preference between friendly soldiers and friendly civilians. In the second technique, known as uncertain ordering, you have several lists of absolute preferences, but each one has a probability attached to it. Three-quarters of the time you might prefer friendly soldiers over friendly civilians over enemy soldiers. A quarter of the time you might prefer friendly civilians over friendly soldiers over enemy soldiers. The algorithm could handle this uncertainty by computing multiple solutions and then giving humans a menu of options with their associated trade-offs, Eckersley says.
How certain are they that giving algorithms a sense of uncertainty is a good idea?
I think, therefore I am; I doubt, therefore I feel.
"There is more worth loving than we have strength to love." - Brian Jay Stanley
What about a friendly city and police?
Criminals need to be detected on CCTV and not get away. That needs a system that can get great results every day and night.
Another person to add to criminal statistics that year as the CCTV and software worked as expected.
Bad people in bad parts of a city do crime. Find them and everyone wins.
A criminal is not doing another crime.
The police, court system and prison system workers have more work to do.
The CCTV and computer tracking system production line gets more sales. Thats workers and professionals with great jobs.
Win, win, win.
Use CCTV and all other tech to find, track and arrest criminals, illegal migrants, people doing bad things in city streets.
Find the tent city, the parked RV, the open drug use, the people placing trash and waste all over a city street.
Making good tech less functional due the politics of the result is not going to help police keep a city safe.
Design the best quality tech and let police use it to track crime, illegal migrants and criminals.
Then nice inner city areas might have some ability to attract new investment again.
Domestic spying is now "Benign Information Gathering"
So in other words, we have no idea how to model human wisdom and decency.
... a little drunk walk?
It little behooves the best of us to comment on the rest of us.
I place a value on each type and the requirement to win the war with the lowest cost. My soldiers cost w, my civilians are x, their civilians are y and their soldiers are z. w>x>y>z. The only thing that I see that is a problem is most politicians or bureaucrats will set w=x=MAX_INT, y=z=0. That's not an AI problem at all but a fundamental problem in democracies.
they deserve it
Cant write code for that.
[($)]
Giving them a menu just moves the liability from the programmer to the operator but evolution tells us the answer; whoever owns/controls the machine will choose self-preservation unless by sacrifice a greater good for the operators social circle (a value which diminishes exponentially the further removed from self another person is) can be achieved. You'd have to program in the operator's entire social structure and ethos into the machine before taking it out.
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Or would that make it more of a heuristic?
Fuzzy logic is not uncertain, in fact there are no stochastic processes at all in fuzzy logic.
"His name was James Damore."
The algorithm could handle this uncertainty by computing multiple solutions and then giving humans a menu of options with their associated trade-offs
That's not the algorithms adding uncertainty and making ethical choices, that's a human performing this task and the algorithm being demoted. The idea that having a human involved improves the ethics of the decision is laughable.
Scoring systems (which are based on algorithms) produce values, not just true or false. If you act like all positive scores are the same (or over your threshold or whatever) then it's not the algorithm that's failed, it's the logic. The problem isn't the programmer who implements the algorithm that's the problem, it's the programmer who makes use of it incorrectly.
"You're right," Fisheye says. "I should have set it on 'whip' or 'chop.'"
That was done when a human designed and implemented the algorithm.
First step to making a depressed robot!
What's wrong with Operational Research and nonlinear derivatives?
People have solved for competing criteria for something like 60 years.
It's a small world and it smells funny; I'd buy another if it wasn't for the money; Take back what I paid (SoM)