So while some ethnic minorities might be more likely to match incorrectly to a known person of interest, they're also more likely to be let through if they are indeed that person. I think that the first case is definitely more damaging on the whole, but I still find it misleading to not mention the specific scope of the "racial bias."
The fact that it produces errors in both directions just makes the system even worse in total.
It's almost like the purpose of the article isn't to show the tech is overall empirically bad.
If they're disproportionatly committing crimes, skin color becomes a strong prior. It would be rational, iff the goal is to lower crime rates. But I don't know that it is morally OK.
Also I'm not sure if over representation is the source of bias in this particular case, haven't read the article yet.
Edit: yeah, this article is just a smear, wapo is salivating over anything they think confirms that police are racially biased. It doesn't mention anything technical about the training data or typical nets being used. I'm not advocating for facial recognition tech, I'm personally against it, but racial bias coming from objective data is the least of your potential worries.
The reason it's relevant is that false positives are always a trade off against false negatives, so the "easiest" way to reduce false positives is to increase false negatives. But false negatives are also very bad, so we can't really do that or we're just trading one problem for another instead of actually solving anything.
The only real solution is to improve the overall accuracy of the system, but that's easier said than done. Some of the main sources of the inaccuracy are intrinsic. The population in question is a smaller proportion of the general population so there is less training data available. People with darker skin absorb rather than reflect light, which makes it harder for the system to identify them.
So the problem isn't some racist schmuck who just needs to be fired and the system will get more accurate, it's a consequence of demographics and physics. There may be a solution somebody can find, but there isn't guaranteed to be, and in the meantime there's not a whole lot you can do other than trying to find one.
Or maybe discontinuing the system entirely.
I don’t mean to imply that’s the right metric at all, of course, because it specially privileges minorities. The proper measure should equally weight the algorithm’s performance on all people in the population, or at least weight them irrespective of race. That’s just not a measure of fairness between races.
Some might object to the decreased accuracy this gives whites — but if we’re going to have any hope of preventing existing systemic inequalities from being encoded in AI systems, we’ll have to socially engineer the data sets.
So to fix systemic inequalities for some groups, we're going to increase systemic inequality for certain other groups?
Definitely a step in the wrong direction as it's just going to swing hatred back the other way. And N-levels later of this "fiddling" you'll end up in a spot where neither side can reconcile with the other because each side has legitimate reasons to feel slighted and no one can look past that "hatred". Just look at the mess that is currently in the middle-east after people "fiddled", and then "fiddled" again to try fix it.
No; in order to accurately identify people through appearances, we'll try to sample evenly throughout the range of appearances and features instead of sampling based on proportions within the population of the people setting up the system.
You're can't be seriously making the case that white people are such an overwhelming portion of the world's population that not concentrating on them in your training data constitutes racial discrimination. Not only racial discrimination even, but is a attack that will inspire a "legitimate" counter-retaliation.
This has wider implications and tacit-meanings that I think I picked up on and definitely implies doing more than just having an equal sampling of each racial group. How that would be enacted, I don't know. The point is to not swing the pendulum the other way in order to "fix things" as it'll cause problems down the line, but to rather just "fix things" in the most non-intrusive and fair way.
Yes, it's kinda unfair on some level that the algorithms used don't work so well on women/blacks/children/elderly as well as "white middle-aged men", but it's not unfair in the "government should do something about it" kind of way just yet. If someone gets misidentified, that doesn't mean police should just go out and "arrest" them. Rather we add a person into the mix and have them talk to that person, make a final call. That is actionable counter-case data that we can use to resolve the bias in the training sets.
A lot of facial-recognition systems include human components in order to resolve errors. E.g. I've worked with facial-recognition software here in South Africa that was developed by Asian companies where the majority of their data-sets and tests were using Asian individuals, so one has to manage error risk using humans in the loop.
One of the things you have to look at is "how" that bias may manifest and if bias can even be applied in any negative sense in the realm of control you give to the user. If you give someone a task where they are to compare two faces, and they don't like the demographic of that individual, I suppose they might pick incorrectly (because you only gave them true/false type options). But you can build in review processes, reporting, performance feedback, etc to mitigate that. You need not treat it as bias at all, merely bad performance/mistakes that need correcting.
I'm not sure adding more humans to the mix is a solution to solving bias problems because humans are unconsciously biased and may be perversely incentivized.
There are many problems with socially engineering data sets. For one we would need to achieve consensus on what constitutes an appropriately representative (i.e. socially just) data set. And then we need a feedback loop to make sure that our dynamically changing inputs don’t knock our meticulously tuned output out of whack. And we’d need to continually adjust our model based on our changing definition of social justice. Finally we need to find social engineers with credentials in building perfect societies (I most certainly don’t trust Joe PM) to perform all this work.
Sociologists have been arguing forever that humans are inherently racist. Maybe these “AI systems” are just proving their point. Perhaps this bias is more a reflection of our inability to maintain global context that is also maximally relevant within our local tribes. Maybe we should focus on acknowledging biases and working through them rather than trying to transcend into some unicorn world where everybody has all the context all the time and bias is impossible.
Anyway, I see your census and raise you an FBI: white people commit way more crimes in the US [1]. So if your goal is to reduce total harm then social engineering is not your solution. Perhaps more accurately identifying the most frequent perpetrators (in the case of the US, white people) is exactly how you’d end up tuning your system...
1: https://ucr.fbi.gov/crime-in-the-u.s/2010/crime-in-the-u.s.-...
The difficulty with this is that you commonly don't have any control over the training data. Someone else collected it and you can use it but you can't choose what's in it.
And in some cases you already have census-level data, which means you couldn't even increase the number of people with lower representation in the data set by collecting more data, because there are no such people not already in the data set. The only way to have equal representation would be to throw out data you do have on the groups with higher representation. Which is obviously objectionable because we need to be improving accuracy for the group with lower accuracy (if we can), not reducing accuracy for the group with greater accuracy just to make the numbers the same.
Are they? Isn't our entire justice system premised on the idea that false negatives are far more acceptable than false positives? There's a problem when your false negatives are systematically racially biased, but beyond that, false negatives seem like what you'd expect and desire from such a system.
The principles of the law makes it clear that it's far, far worse to have an innocent person in jail that a guilty person go free. Many legal principles revolve around this.
Yes, absolutely. But there are two issues in this context.
The first is that facial recognition isn't a conviction. Presumably if it identifies the wrong person then they'll have the opportunity to demonstrate who they are, e.g. by showing that the name on their ID is not the name of the suspect, before being convicted of the crime.
And the second is that ordinary application of that principle is incompatible with eliminating the disparity, because if you shift in favor of false negatives in general then it reduces the false positive rate for all races but the proportions stay the same. But if you shift it only for one race then you're only making the existing racial disparity in false negatives even worse, which is hardly fair to minority communities because it implies they'll have a higher proportion of criminals on the streets victimizing their communities.
That being said, in this context having a preference for false negatives over false positives in general probably is a good idea, and it does reduce the absolute (rather than proportional) racial disparity in false positives -- which is not nothing. But it's the proportional disparity that people are always complaining about.
Briefly, bias is the wrong mean, and inaccuracy is high variance.
So, by your statistical definition, there's a bias.