Even without the bias issues, if I were a hiring manager how do I verify that the weights "MagicHireAI SaaS Corp" are using aren't leaving swaths of talent on the table that I don't even know exist?

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.

I know I have to.

I know I don’t have to.

I don't understand what this means. Did she have to turn on her webcam to upload her cv? How does that even make sense.

Also, how is that legal? I thought requiring a photograph to apply for a position is illegal (except for stuff like acting).

In the US, it is illegal per the EEOC, which has barely any resources to actually enforce the law: "Similarly, employers should not ask for a photograph of an applicant. If needed for identification purposes, a photograph may be obtained after an offer of employment is made and accepted." [1]

[1] https://www.eeoc.gov/prohibited-employment-policiespractices

Asking for a photo opens an employer to claims of discrimination, but it would still have to be proven, so the government is advising against it. But it may well be that in practice, the government will not be spending resources on trying to or be able to prove discrimination, even if a photo is required.

  > Now with all those aptitude training [...] that require you to turn on your webcam

The problem here is that companies have now gone from identity verification to this 'aptitude test' ML bullshit that's just another cover for increasingly weird arbitrary discrimination against applicants.

It doesn't benefit genuine candidates to blatantly lie. They'll get caught later in the process. But you're assuming identity thieves are acting rationally or fear getting caught. They don't, and they're playing a numbers game. If you get past an interviewer and get fired after two weeks, your salary at a US tech company can be $5k, which is the average annual salary in many developing countries.

Don't pay for this cruft, sponsor local meetups instead.

White noise contains all the frequencies. Black body radiation is radiation due to heat emitted from an object which does not reflect any light (is black).

That is the etymology of whitelist and blacklist.

It's hard finding a definitive source for blacklist, though first I've read said to be from that sense. Many centuries old examples I've seen when looking this up seem to have already had it as some in-use vocabulary.

Edit: huh, entire parent thread has been entirely hidden on HN. I suppose it was too much of a digression.

Still, nothing to do with black or white skin or any bias against people of different skin colors, so not contesting your actual point.

I would have thought it would have something to do with darkness/night versus bright days/day, where people experience more danger in the former than the latter for obvious reasons.

I do think there is some validity to the change.

Black lists are lists of bad things that need to be blocked or prevented, white lists are things that are good and allowed and wanted. That does create an unsavory connotation with the words themselves.

Furthermore, as someone in an industry that used blacklist and whitelist extensively, noobs screw up those words all the time. It’s not a very clear word. Blocklist and allowlist are much less ambiguous.

Whitelist and blacklist are indeed ambiguous, and they also create negative connotations that could reinforce other, actually racist views and language, whether conscious or not.

But hey, i can do that, too:

> no matter the actual origins of the word in question

> The only way to not be seen as a racist is to apologize and use whatever words they tell you to use.

Please stop using the words "debating", "win", "game", "oppose", "racist", "apologize". They are highly offensive and racist vocabulary.

"words have meaning": this is a right wing dog whistle, even if you are a true language prescriptivist no one will believe you.

"it's stupid": just makes you look like an asshole when someone is trying to do something people see as in good faith.

"it's pointless": then why does it matter one way or another? Goto other reason.

"it's not racist": when we're talking about blacklists it never was, it's about trying to not always associate black with the negative connotation, but you look really racist for wanting to keep it.

"black outside the context of race does have negative connotations in humans, you can't change that we're afraid of the dark": now you look really really racist.

So basically you're "proving" to yourself that everyone is either racist or an asshole, by creating a situation where people don't want to listen to you because you're insufferable. But you can pretend to be virtuous because you're doing it for some make believe greater good about the word "whitelist."

BTW I'm actually trying to help you see the reason there's resistance. I use "allowlist" and "denylist" voluntarily in my OSS repos, because it doesn't really matter to me. But I still find people who make it their moral imperative to police these words insufferable. From my perspective, you either don't know that, which shows you have no tact, or you don't care, which shows that you're doing this in bad faith.

> When was the last time you told someone not to use a word

Literally never. Again you assume a lot about me. I am speaking purely from the perspective of being told to use different words. It's never really bothered me. My response has always been "sure whatever." Some of them are a stretch but a lot of them make sense.

Believe it or not, people are more likely to be put off by obnoxious word police than they are to be racist.

Okay let's go back to the beginning here.

> The more you oppose someone telling you not to use a word, the more racist you appear, no matter the actual origins of the word in question.

And I expounded why -- literally agreeing with you. Then you called me insufferable. If people took "you're obnoxious" as a valid defense then there would be a way to win the game. Your entire point is that all roads lead to racism and then got defensive when in fact all roads lead to racism. As much as it would be fun to be an omnipotent benevolent dictatress, am I in fact not and have no power to change how people behave.

Also you're clearly mad about word policing but your gripe with them is that you think the way they deliver the message is obnoxious and insufferable. But since you did voluntarily switch up your wording you clearly have no issue with the message itself. Which means you're tone policing. I bet some folks view you the same way you view them.

Whoever cooks up these schemes should have put more effort in. Even something like passlist and stoplist is easier to say. Throughlist. Something that sounds nice in a foreign language, maybe.

You may also be biased towards whitelist and blacklist if you’ve heard them your entire life - if you had grown up with allowlist and blocklist, can you say for certain that someone suddenly saying “we should use whitelist and blacklist” wouldn’t give you the same feeling of clunkiness?

Both terms of phrase have just as much racist connotation which is to say none.

I seriously 100% agree.

You’re right. They exist in the context in which they’re used. In this case that context is as a technical term that has no association at all to any racist ideas. I would advise you to consider that you do not exist in a vacuum, and perhaps you should reconsider your anti-social impulse to police other peoples’ use of innocent words. Forcing people to remove words from their vocabulary because they remind you of some upsetting concept is not inclusive at all. It is by definition exclusionary.

Renaming things or describing them differently does not change them. As an adult citizen in a democratic country, you should be able to see through such manipulative moves.

I agree with the other posters making fun of your logic, because its arbitrary and can be used equally on your own writings.

Haha, ok buddy.

If you want to be more inclusive, perhaps you could go to the effort of understanding the vocabulary of my trade and the meaning of those words, rather than try to prevent me from using them. Because I’m feeling very excluded by you right now. Do you care about the lived experience I’m having right now? If not, why?

The term whitelist is a legitimate piece of technical jargon, and has no racist origin. That is the end of any reasonable discussion on this topic. If you wish to litigate that point any further, that only direction that could possibly take is into wildly unreasonable territory. Renaming anything that people may feel is associated with other problematic concepts has no finish line. There’s an infinite quantity of problems you could invent using this approach, and all of the would be completely equal in their unreasonableness. The only way you could potentially limit this scope is to elevate the unreasonable feelings of one group over the unreasonable feelings of all other groups. Which is an exclusionary practice, and goes to show how subversive the use of language like “inclusion” is here.

While the direct impact of changing a term might seem insignificant, it's about mutual respect and understanding. This isn't language policing, but sparking awareness. Your feelings are valid, your input is welcome. Let's keep talking, even amidst disagreements. Progress lives in dialogue, not silence.

A case could also be made for people who aren’t aware of the context: are they confused because the terms aren’t familiar? Does their own bias influence them to understand the meaning and infer white = good, black = bad? If so, is that not more reason to change the words? Shouldn’t we move away from language that associates good and bad with colors that are often used as tools of division?

Regardless of who the audience is, the terms equate a color to either being good or bad and it’s kind of silly when terms like allowlist and blocklist are immediately parseable into what they mean.

I don’t particularly care other than if there’s some tiny segment who feels the terms could be even remotely offensive, and we can all agree that regardless there are better terms, why not use them?

A core lesson in ML: this is difficult or impossible. See the classic example with wolves, dogs, and snow. [1]

For a resume example, suppose you don't want to be biased again black candidates, or women. Well, good luck trying to filter out resume experience, skills, schools, and interests so that they don't list anything that is statistically more likely to be found among black folks or women. It's impossible.

So now you have a hiring process that looks at... what exactly?

[1] https://www.researchgate.net/figure/A-husky-on-the-left-is-c...

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 is that variables where you can safely discriminate don't exist in the real world because everything is intertwined.

For example, if you see that somebody went to school at {Howard, Bryn Mawr, BYU} you can make some pretty solid inferences about their {race, gender, religion} even if they don't explicitly provide them.

Howard is a historically black college, which means their student body today is majority black, not 100% black.

Bryn Mawr is a womens' school and admits only women. However, gender is not always fixed going through life.

BYU is a Christian school, but also admits several non-religious people too, and people regularly become apostates to their respective religion.

Technically true of course, but >99% of the population will identify with a single gender throughout their life. The correlation is so strong that it's not even really a correlation anymore.

That doesn't mean you aren't stereotyping people, though.

If the training data shows a history of bias against Howard graduates, the model will learn to replicate.

Job applications are kind of dumb. The common use case is credit decisions. Surely, for example, you’re ok looking at credit history and income when deciding to make a loan, right? Even though it won’t be equitable outcomes across races, it is still “fair”

Other goals can be to have equal model accuracy across subgroups (e.g., models has the same precision/auroc/etc for each subgroup, even if the predictions themselves differ across subgroups) or identical distribution of statistical biases across subgroups (e.g., the model makes the same types of mistakes within each subgroup)

Like, let’s look at that for credit decisions. Black folks have a lower income distribution. So it’s like saying the model is ok if it rejects a Black applicant making 80k if it would also reject a White candidate making 100k. That is the insane bullshit you’re effectively supporting by desiring comparable error distributions.

You have to accept that equal outcomes are not just impossible but not desirable. And then you only use fields that people agree are fair to use. If Black people get disproportionately better or worse outcomes it is either because those outcomes make sense given their stats or it’s not ok because if a White dude had the same info, that you agree is fair to use, they would have passed and the underlying reason for the difference is some bullshit other input.

That might mean that the model is a dumb: you must make $100k for this loan (illustrative) which is going to mean a lot of Black folks cannot get it. Well that model is at least “fair”. Accommodations for race should not be coming from an AI model

For example you can infer the race of a candidate from other facts about them. The model could still illegally discriminate by first inferring race, then discriminating on the basis of inferred race.

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