What If Algorithms Could Be Fair?
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What If Algorithms Could Be Fair?
1–10 of 33 posts
Re: What If Algorithms Could Be Fair?
#2I'm not sure I follow their car crash diagram and explanation. They've laid out that one ethnicity might prefer red cars more than others, and drivers of red cars tend to get into more crashes, and that training ML with "red cars" as a feature would lead to a bias against that ethnicity. I got that part. What I don't get is how the creation of the "risky behavior" node can be assumed to have a completely uniform distribution of ethnicities inside of it. The author has no problem saying that an ethnicity can have one causal behavior (purchasing red cars) but not another (being riskier drivers). This seems logically inconsistent.
Re: What If Algorithms Could Be Fair?
#3Are there no fair algorithms? I urge the authors to at least give Bogosort another chance!
Re: What If Algorithms Could Be Fair?
#4I'm not sure I follow their car crash diagram and explanation. They've laid out that one ethnicity might prefer red cars more than others, and drivers of red cars tend to get into more crashes, and that training ML with "red cars" as a feature would lead to a bias against that ethnicity. I got that part. What I don't get is how the creation of the "risky behavior" node can be assumed to have a completely uniform dist…
Right, it seems very plausible that car culture differs in different cultures. Is it truly unreasonable to suggest that perhaps more than an average number of Italians are fast aggressive drivers? From what I've heard and seen, it's anecdotally true. I wouldn't rule out the possibility of it being a statistically true.
And every time I express my desire for autobahns without speed restrictions to crisscross North America, whoever I'm talking to has generally been quick to inform me that Germans can have nice things like that because they are careful/skilled drivers, while Americans are reckless (wreckful) drivers and cannot be trusted at high speeds.
Re: What If Algorithms Could Be Fair?
#5If the algorithm charges higher car insurances premium to men, does it mean it is fair?
Re: What If Algorithms Could Be Fair?
#6I'm not sure I follow their car crash diagram and explanation. They've laid out that one ethnicity might prefer red cars more than others, and drivers of red cars tend to get into more crashes, and that training ML with "red cars" as a feature would lead to a bias against that ethnicity. I got that part. What I don't get is how the creation of the "risky behavior" node can be assumed to have a completely uniform dist…
There is a strong push for "fairness", see e.g. "Toronto Declaration". I think all it would do is completely halt progress of AI and install bureaucracy to the lowest decision levels, paralyzing whole ML research. Nobody seems to think that we are in a clash of different cultures with different sensitivities and there is no single common platform for stating what is "fair". I am worried the loudest voice would set the trend and we will have some insanity enforced all the way down. There are even calls to ban "blackbox" ML, basically allowing only trivial parts in any kind of decision making.
If members of my nation get drunk more often than some other, while it's offensive to say I am a 34% drunkard, on average it might hold; instead of forbidding this type of inference I'd rather rely on more signals to figure out what kind of person I am specifically for individualized decisions. They bypass this problem by adding "risky behavior" not contained in the input dataset so they just decide to model it as a hidden variable of Bayesian inference, where "risky behavior" might be correlated with ethnicity and red car anyway, just not visible outside. So if my nation is 34% drunkard but neighboring is only 11%, the conditional probability will likely be higher for my nation anyway, but obfuscated by the use of Bayesian hidden state. I am not sure why would that improve fairness.
Re: What If Algorithms Could Be Fair?
#7I'm not sure I follow their car crash diagram and explanation. They've laid out that one ethnicity might prefer red cars more than others, and drivers of red cars tend to get into more crashes, and that training ML with "red cars" as a feature would lead to a bias against that ethnicity. I got that part. What I don't get is how the creation of the "risky behavior" node can be assumed to have a completely uniform dist…
I think the two behaviors should be understood as arbitrary for illustrative purposes. The point is, as I understand it, that you can decide that one causal relationship exists and another does not, and derive a model consistent with that and with the observed statistics.
Because, as people give lip service to constantly, but never seem to really adhere to, correlation is different from causation.
Re: What If Algorithms Could Be Fair?
#8If the algorithm charges higher car insurances premium to men, does it mean it is fair?
I think the whole point is that what causal relationships you assume matter, and they do not have to be derived from correlations. And they should not, in order to be "fair".
You have a choice of whether or not you believe being male causes car insurance claims. That is independent of the statistical correlations. Ten times a day people say correlation is not causation, but a hundred times a day, I see people implicitly insisting that it necessarily is.
Re: What If Algorithms Could Be Fair?
#9I'm not sure I follow their car crash diagram and explanation. They've laid out that one ethnicity might prefer red cars more than others, and drivers of red cars tend to get into more crashes, and that training ML with "red cars" as a feature would lead to a bias against that ethnicity. I got that part. What I don't get is how the creation of the "risky behavior" node can be assumed to have a completely uniform dist…
There is a strong push for "fairness", see e.g. "Toronto Declaration". I think all it would do is completely halt progress of AI and install bureaucracy to the lowest decision levels, paralyzing whole ML research. Nobody seems to think that we are in a clash of different cultures with different sensitivities and there is no single common platform for stating what is "fair". I am worried the loudest voice would set th…
"forbidding this type of inference"
Isn't this just a misleading way to say "holding a certain causal belief"? Why exactly would that be a bad thing? If you reject one set of causal beliefs, you necessarily hold a different set.
Re: What If Algorithms Could Be Fair?
#10I'm not sure I follow their car crash diagram and explanation. They've laid out that one ethnicity might prefer red cars more than others, and drivers of red cars tend to get into more crashes, and that training ML with "red cars" as a feature would lead to a bias against that ethnicity. I got that part. What I don't get is how the creation of the "risky behavior" node can be assumed to have a completely uniform dist…
Right, it seems very plausible that car culture differs in different cultures. Is it truly unreasonable to suggest that perhaps more than an average number of Italians are fast aggressive drivers? From what I've heard and seen, it's anecdotally true. I wouldn't rule out the possibility of it being a statistically true. And every time I express my desire for autobahns without speed restrictions to crisscross North Ame…
If more than an average number of Italians are fast drivers, it doesn't mean being Italian causes being a fast driver. Is the idea that correlation is not causation in this context really breaking everybody's brain?
Now you may argue that correlation reflects causation in a particular case, sure, but in general, it is not the same, so it seems perfectly logical to me to point out that you can start building your model with certain causal assumptions and without others, without in any way disregarding your statistics.