New York City’s AI hiring law takes effect

But I feel personally obligated to just note, for any HNers reading, that the two ex-McKinsey people I've worked with closely at a startup were both decent, and I'd work with them again.

Heh, maybe we can get McKinsey on our side, on this particular AI dystopia threat. Some hypothetical vendor-selection AI model (comparable to a hiring one) might take one look at certain past scandal involvements of the firm, and denylist anyone associated. But even organizations that don't care about individual fairness might still have to care about some AI pet project sabotaging the quality of their people and vendors.

Like most other things companies do in the hiring process (e.g. endless redundant interviewing rounds, ridiculous culture fit questions) -- or the stuff they do in their day-to-day work ("Agile") -- it doesn't need to actually "work" for it to be widely adopted.

It just needs to appear to work, and to give off a warm, buzzy feeling.

I would think that's one of the primary concerns here, is that we can pretty much guarantee that it doesn't work. Then end result would turn hiring processes into a kafka-esque hellscape where reason has no bearing and candidates are essentially selected at random.

Given how infuriating the current crop of simulated customer service agents are, I can't imagine bringing anything like this to job selection would be good for any of us, save for the leadership team who gets to tout their cost-saving plan and give themselves a bigger bonus.

Edit: ugh after reading the article I'm so disappointed that the regulations amount to some pretty weak auditing and transparency requirements. I was hoping this was an outright ban. It seems the laws have been rewritten by the cooperations. It gives candidate "right" to ask questions about their use and possibly opt out, but all that means is anyone who speaks out will be unhireable. I mean what company is going to hire anyone who starts asserting their rights up front?

I think the entire history of business backs you up on this.

That seems like harsh characterization. If all of the candidates are poor matches then perhaps the recruiting system isn't working properly.

Most companies don’t operate anywhere close to this scale and should not imitate this same hiring process.

The amount of stuff they make you fill out that is then parsed with AI is insane.

Indeed ruined a lot. Before if you had an ok resume but we're able to talk to them in person you might be able to convince them.

Now with all those aptitude training you have to do well there.

Not to mention the applications that require you to turn on your webcam (!) while you do them. Disgusting.

That last one by the way was the same company where my wife had an in person interview. She drove there. The interview was on Zoom with somebody in another office.

She declined the offer. Wastefulness and already showing that they're not against monitoring by AI means 0 humanity.

The whole point of white listing variables is to only allow things you are comfortable using todiscimrinate. And yes, that likely reduces the power of the model.

Edit: to clarify, your example had the model failing to classify something correctly because the training data did not generalize across real world examples. The problem with diversity bias is that the modes are trained on past data where the labels themselves tend to have some bias. Black candidates do worse. So recreatingthese will generally produce worse outcomes for minority candidates. Now for a lot of these cases the average minority individual is more likely to do worse. Not intrinsically because of race, but still worse.

You can’t realistically expect the model to produce equal outcomes for all classes when all classes are not equal on the actual traits. But you do want to try and make it so that the model doesn’t use race indicators as a crutch that ignores actual good indicators. The common complaint is you hear about a man and a woman applying with the exact same credentials but different outcomes. Well obviously the model had to have additional information besides those credentials if it made different decisions. If this causes problems, you probably should not permit those additional pieces of information.

The problem you describe affects blacklists that try to remove only the things you obviously don’t want to use.

Which part of the article did you skip?

Start with the part where AI hiring tools favor people named "Jared."