Posted by sizzle 5 hours ago
I then went on to work for multiple firms that placed a premium on candidates from Ivy League/Top Tier (Stanford/Duke etc) candidates.
This taught me that:
- Their are pros and cons to any selection criteria.
- There are smart people everywhere. One of the smartest people I ever worked for spent several years in prison for drug dealing. He was on par with many of the Managing Directors I've worked for
- There was a study where they asked big bank recruiters which school consistently produced people who were excellent employees 2-3 years out from hiring and the answer was Penn State (not my alma mater)
- There used to be "manager's choice" hires where managers had 1 slot in a training program where they could select whoever they wanted. Sometimes that was terrible. Sometimes that person was top of their training program.
- Smart people are just as capable as creating problems as less intelligent people. Smart people, in some ways, are better at creating problems. Especially if the incentives reward them for creating those problems.
If we move to using just a small number of AI models to help do things like hiring, we will amplify biases and possibly completely lock out portions of the population. We need to be very careful when using AI systems to evaluate people in general -- not because they might be biased (which they might be), but because even a small bias, if used by virtually everyone, can be damning.
I don't think this even requires any bias.
Assume there's some loose ordering of who is or isn't a good hire, and every employer has their own fuzzy view of it. If you get slightly better or worse as a potential hire (pick up an extra degree, let your latest certification lapse, whatever), it gets somewhat easier or harder to get hired.
Now assume that same ordering, but all employers share the same view of it. I'd expect the divide between employable and not employable to be much sharper.
Also, we will of course have all kinds of attempts to "game" the system to get ahead. Optimizing (even more) for the metric. Degree mills, for instance.
You might be able to avoid it with a panel of AI, similar to how we try to avoid it by using panels of humans, but even that turns out to be contentious and not surefire.
I have feeling with AI it'll be even worse, since folks / companies can pass the buck (similar to how health insurance companies are now using it to deny folks).
Unless you're taking the "there are multiple mathematically incompatible ways to define bias" view of the topic, just do what's already known best practice for high-bureaucracy human review. Which is too define an overly-pedantic standard rubric.
However, "this AI model can decide that some subset of people, perhaps random, perhaps not, are simply not hirable for any job" makes sense to most people regardless of political bent.
For example, the article here shows disparate impact: that different percentages of applications are passed through the AI filter. But it doesn’t show differential treatment of otherwise identical applications based on race.
But LLMs are statistical models. They are aggregating all biases into a general super bias. And they're all converging towards the same solutions.
Definitely open to opposing or critical views
If you click through, the paper says the race is self-reported.
“Our data tracks 4,197,168 applications. It includes applicant gameplay features and for each application, the application date, the position name and employer, metadata about the position and employer, and the numerical score and final recommendation each applicant received for each completed application. 40.2% of applicants self-report race with a breakdown of 16.8% Asian, 14.2% White, 3.6% Black, 3.0% Hispanic, and all other racial categories below 2% (i.e. fewer than 100,000 applicants).”
The dataset is constructed, deliberately, to hold candidate performance constant and vary the names of candidates to appear to be associated with a specific race.
But they picked 9 family names per group. Which sounds quite low. And combined that with first names to reach 500 first+last names per group.
I wonder how much of the bias we see has to do with the names actually picked versus it being racially motivated (absolutely not denying that this probably is a factor, but might not be the only one).
For example, in France there is the national BAC end of high school exam. If you you at the names X grade distribution, and look at the higher “very good” bracket: some names are heavily under-represented (less than 5% of say “Jordan” get that grade) while some are over-represented (35% of “Josephine” get such a grade). The exam is for the most part anonymous, but some names are definitely heavily correlated with lower/higher income groups. So nothing surprising: Josephines tend to come from richer families, thus in average get better education/support, thus better grades. Same thing is true with family names to a smaller extent.
So I wonder how much of the bias we see, be it from real persons or the AI has more to do with a class thing than a racial thing. Again those are not neatly separate things, but still
Cool.
In any event, I'd happily support a ban on all parts of the ATS that could be involved in automated approval, rejection, or scoring being able to see candidate names. But I sense the author of this has a bigger agenda.
its going to be in the rest of the data because race has a meaningful correlation, and pleanty of causation with being disadvantaged in real ways, that can also affect the ability to then do certain jobs.
like, the environmental pollution and building interstates and freeways through black communities, on purpose to do bad things to those communities, then results in a bunch of noise and particulate pollution, that is bad for developing brains.
you wont be able to do some meritocratic non-racist hiring without fixing the environmental racism. otherwise youre just mirroring racism other people built for you
I'm not saying AI is not biased, but this study does not prove that.
[0] https://arxiv.org/pdf/2605.27371
From the paper:
> Fig. 1. The pymetrics process. > Stage 1: Applicants apply to positions. > Stage 2: Applicants are directed to the pymetrics platform to play assessment games. > Stage 3: pymetrics algorithms use applicant gameplay features to recommend 58.2% of applicants per position on average. > Stage 4: Employers decide which applicants to interview or hire, typically rejecting applicants that were not recommended by pymetrics.
> Our research also found that this pattern does not appear to be the case in other circumstances. We analyzed data from the largest prior study of hiring decisions, which sent 83,000 applications to 108 Fortune 500 firms during the same time period as our study and did not focus on whether AI was used to make decisions. We found that the rate at which applicants were rejected from every firm they applied to in this data was no higher than what you’d expect if each company decided independently of the others.
It sounds like this study was using real-world applicants, and the other study they're comparing against was using synthetic applicants.
Consider the chance of being accepted as being composed of signal+bias+noise. Noise is random. Signal is a per-applicant value, and what's meant to be measured. Bias is a per-group value, and an artifact of the measuring process.
If acceptance/rejection is independent between positions applied for (as in the synthetic applicant study), that suggests that it's random or composed entirely of noise; ie there is no signal; ie the applicants are all equally qualified.
If acceptance/rejection is correlated, that means there is some nonzero amount of (signal+bias). But real-world applicants are not all identical, so there should be some amount of signal. So you can't just assume zero signal in order to infer that there must be bias.
A inferior candidate (by skill) is going to be consistently rejected, no?
We find applicants are more likely to be rejected from every position they apply to than would be predicted by the baseline of each position making statistically independent decisions.
Obviously a rejected resume is more likely to be rejected by every other employer and an accepted resume is more likely to be accepted by every other employer. Like online dating, most employers are looking for some baseline indicators that you are going to be successful and stable.
But that wasn't the case for non-algorithmic screening. From the paper:
"By contrast, we find that when first round screening is not mediated by a single screening procedure, systemic rejections are close to the baseline. To support the empirical validity of our baseline, we study homogeneous outcomes in the largest study of first-round screening at U.S. employers to date. Kline et al. [38] generated 83000 synthetic resumes and submitted these resumes to vacant positions at 108 US companies between October 2019 and April 2021, a similar time period to our data. The companies, which are a subset of the Fortune 500,15 collectively employ 15 million workers. We analyze the homogeneity observed in the resulting callback outcomes in their data. We find that the baseline is an effective estimator of the systemic rejection rate for this dataset. As shown in Figure 3, the observed systemic rejection rate is accurately predicted by the baseline and a chi-squared goodness-of-fit test cannot reject equality of the two distributions (2 = 20.05, = 0.69). In other words, while the largest previous study observes systemic rejection rates consistent with employers making statistically independent decisions, the algorithmic hiring data shows significantly correlated outcomes that lead to higher-than-baseline systemic rejection rates."
Actually the fact that they found this result didn’t hold in a different dataset is especially weird.
This makes sense to me, albeit intuitively and in a way I can't articulate.
> an accepted resume is more likely to be accepted by every other employer
but this doesn't necessarily follow from the prior for me. Plenty of people get really good jobs and are really successful in them only after dozens or hundreds of rejections with a nearly-identical resume.
I just think certain resumes will get an interview almost every time in some industries and certain resumes will likely never get an interview almost every time, but the majority of resumes are like you say have different aspects that appeal to one empoyer over another.
High-risk – AI applications that are expected to pose significant threats to health, safety, or the fundamental rights of persons. Notably, AI systems used in health, education, recruitment, critical infrastructure management, law enforcement or justice. They are subject to quality, transparency, human oversight and safety obligations
That's a pretty common sense legislation to me.
There's no reason to single out AI vs any other approach to the same topics.
Of all the things listed "recruitment" doesn't belong to me. Is the argument that it is someone's fundamental human right to get someone else to pay them to do a job? Or is it strictly about human oversight?
"Cards held by African-American sellers sold for approximately 20% ($0.90) less than cards held by Caucasian sellers, and the race effect was more pronounced in sales of minority player cards."
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.
it sounds like how you'd get that kind of metric at least
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.
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.
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?
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.
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.
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)
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?
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.
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.
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.
:)
Screened Applications [13]
Unscreened Applications [39148]My point is that this is effectively an auto rejection