That seems like a nonsensical way to measure racial discrimination. What could justify it?
It indicates there may be adverse impact to one group. It specifically is not used to resolve racial discrimination.
It's purely a signal for "we should consider asking more questions, because this appears unusual". That's what your quote says too, it "flags" a low recommendation -- it's indicating further study and investigation is likely warranted.
"Adverse impact occurs when there is (i) practically and (ii) statistically significant disparities in the selection rate for the group of interest when compared against the selection rate ′ of the most selected group ′ . Practical significance requires the impact ratio ... to be less than 0.8, which is why the EEOC guidance is colloquially referred to as the 'four-fifths' rule."
The headline numbers reflect the positions for which the 4/5 rule was triggered, not the result of some further investigation: “We discovered that 26% of Black applicants and 15% of Asian applicants applied to positions where the AI system discriminated against their racial group.” Based on the methodology, I think that means that 26% of black applicants applied to positions that were flagged under the 4/5ths rule.
This doctrine is the basis for much of employment law. It is a significant reason why employers don't administer IQ tests (or equivalents) to screen candidates since ~the 90s.
A common objection to the doctrine is that it leads to unfalsifiable discrimination claims, which is why it seems nonsensical to you.
There is a large body of literature concerning the question "does disparate-impact enforcement cause employers to alter hiring behavior in ways unrelated to actual productivity or discrimination?" and the answer is largely "yes". As you suggested elsewhere in this discussion, Google may be useful.
Are you suggesting that companies should violate the law here? What do you recommend?
Edit: charitably, "adhering to the letter of the law" is sometimes shortened to "law-abiding" and is generally what we want.
Prior to the beginning of your excerpt is the word "You", meaning the comment's author is the subject, not "companies". I'm saying the commenter is appealing to black letter law for the answer to the question "what happens when..." but we have observational evidence to answer the question.
Isn't the point that the observational evidence amounts to the companies in question steer clear of illegal behavior?
There are anti-money laundering laws, so banks institute procedures to help them comply. Yes, we expect companies to change their processes so they don't break the law. That's the point of the law.
I am confused with what you think companies should do in this situation. Expose themselves to legal and civil liability? Or change their behaviors so that close scrutiny indicates they are trying to comply with the laws and any bad actors acted against internal procedure?
You're arguing about something else, using the form "given we know that racism is happening, and furthermore we know where and how it is happening, why shouldn't we just do these Totally Cost Free and Obviously Good Things that are just like fighting money laundering"
Well, I just don't think any of these things are as evident as you seem to assume they are. Also fwiw I grew up in the US, where I was called all sorts of slurs -- like, the real ones you probably censor in your internal monologue when you see them written -- throughout K-12 education. I still don't believe in the existence of pervasive and oppressive racism the way you seem to assume it.
To act like it's bad that people of colour have a more fair chance of getting employed because of some piece of legislation is simply insidious. It's just been over a month since black people lost the right to a fair vote.
Literally the opposite happened. The Supreme Court ruled that there was VRA §2 liability when there was evidence of racially-motivated gerrymandering: "In short, §2 imposes liability only when the evidence supports a strong inference that the State intentionally drew its districts to afford minority voters less opportunity because of their race." (Louisiana v. Callais, p. 26)
If the issue happens upstream of the defendant to a claim - generally an organization being sued by an individual with fewer resources - it incentivizes such entities to push for changes upstream, so that they don't get stuck with the bill.
If you're making the claim you need to provide the evidence.
Most people would say that a persistent disparity means it's possible there is discrimination, but it's not definitive proof.
We have a "disparate impact" and nobody can prove what proportion of it is due to things like parental income or childhood education as opposed to racism on the part of the employer. Because the former considerations are real contributors, the metric can regularly be expected to exceed the threshold even if the contribution of racism by the employer was zero. Doesn't that imply that we're essentially accusing people of racism at random?
> because the impact exists whether intent can be shown or not, the desire remains to ameliorate that impact.
The median household income for Asian Americans of Indian ethnicity is more than double those of Burmese ethnicity:
https://en.wikipedia.org/wiki/List_of_ethnic_groups_in_the_U...
This is objectively a disparate impact and likely shows up in several other metrics in addition to income. Disparate results can almost universally be obtained by arbitrarily segmenting the population into different groups and comparing the midpoints. Americans of Australian ancestry have a higher median income than those of Irish ancestry, Bolivians higher than Cubans. The result is often because the lower down group has a history of being oppressed.
What reasoned means can we use to determine which groups get the benefit of these methods to ameliorate the disparity and which don't? What should be done about the inherent impossibility of doing them simultaneously, e.g. because hiring a South African woman over a Haitian man would reduce the disparity on one axis while increasing it on another? Notice that considering each group separately could result in unconditional liability because either available alternative puts you over the threshold for one group or the other.
> If the issue happens upstream of the defendant to a claim - generally an organization being sued by an individual with fewer resources - it incentivizes such entities to push for changes upstream, so that they don't get stuck with the bill.
Do we want to apply this logic to other things? The median income in California and New York are significantly higher than they are in Alabama or West Virginia and they have higher ranked public schools. We can correspondingly expect that when applicants from different states apply for the same job, the ones from California and New York (even if they're the same race etc.) are more likely to be selected because they had more advantages growing up, even though none of them chose where they were born.
By the same reasoning we should then have the federal government penalize employers for hiring the applicants from the more affluent states so that it "incentivizes such entities to push for changes upstream, so that they don't get stuck with the bill." Does it make sense to do that?
That's the beauty of aggregate statistics. You have some elite job with 100 slots. Before you had 60 of the slots going to cronies and the remainder being allocated on merit. The cronies were disproportionately of the same ethnicity so your statistics are skewed, but don't worry, all the cronies still get their slots. Because statistics can be balanced by getting even more cronies, this time of a different ethnicity, and giving them as many of what used to be the merit slots as you need to manipulate the average. Using statistics is perfect for pretending that you're giving people something when you're actually taking something away.
The assumption that applicants from all races are on average equally qualified for every position. Whole subfields of modern academia are based on that assumption.
I am getting downvoted because it's hard to admit that AI only reinforces the culture that it is trained on. It is the perfect technology to keep systemic racism in place, all while being the perfect scapegoat for lack of personal or corporate accountability.
Individuals are qualified or unqualified. If a company happens to end up with less than 1/4 Ravenclaws or not very many Virgos, it doesn't mean hate is a reason. It could be that the Ravenclaws that applied were a bit less qualified than those from the other houses.
I guess my point is, doing the statistical analysis for race and gender and drawing conclusions, while being completely blind to the one single factor any sane hiring manager should be focusing on -- actual qualifications for the role -- doesn't make any sense.
Don't claim AI is discriminating against non–selects, though.
I doubt companies are using Gr*k to make their hiring decisions.
it sounds like how you'd get that kind of metric at least
Because surely no one would have legitimate preferences based on their gender, cultural norms, etc. or real differences in aptitude due to childhood exposure, education, or said norms and preferences.
Here's some analysis of what it is and why it's useful as a canary in the coal mine: https://www.prevuehr.com/resources/insights/adverse-impact-a...
> Since the 80% test does not involve probability distributions to determine whether the disparity is a “beyond chance” occurrence, it is usually not regarded as a definitive test for adverse impact. Instead, other statistically significance tests, such as the standard deviation analysis, may be used for this purpose.
But then my question recurs: isn’t this a ridiculous way to measure discrimination? It’s assuming that the only thing that differs between the different ethnic applicant pools is their ethnicity, which is essentially never going to be true.
Like. If I am evaluating a developer on lines of code written, I am a bad manager. But if an engineer has 40% fewer lines of code than the team median, it's absolutely ok for me to go, "Interesting. What's the story there? Are they slower or is there some other factor?"
Same idea -- this is purely a fast, first pass metric that can quickly assess if something warrants a deeper evaluation.
I expect Median LoC might be very high with the average developer using AI these days... but the dev who is making atomic changes that are fixing the AI output is probably tiny LoC but way more important
If you are trying to say "more data needed, headline misleading" you should say that instead of misrepresenting the 4/5ths rule. Also the word "can" implies uncertainty of conclusion. This isn't ridiculous, the authors point out that this is the first large scale study of this topic. Nothing has been "proven" here, it's showing that this warrants further investigation and attention.
Do you read many academic papers, because you seem to be having a rough go here.
The authors of this study have done that here. To borrow from your example, if you saw a statistically significant amount of my posts highlighting the merits of the Iranian government in a ways that run counter to the general global consensus of their actions, you would then have something that other people might agree was worth looking into. A hypothesis is not “misinformation”.
This article has not claimed to have proven anything other than outcomes in a process. I don’t understand why this is so upsetting to you.