Darker skin has less contrast between highlights and shadows so isn’t it a more difficult problem to identify them from a technical perspective?
Plus human facial recognition is massively overpowered for normal cases. I think if you tested it in challenge cases (through blurry video, or snow, or in the dark, or at a distance), you might see darker skin tones being recognized less easily. It'd be an interesting experiment.
Citation?
Dark cars have more accidents- https://www.telegraph.co.uk/motoring/news/7845366/Black-cars...
There clearly will be a difference.
Whole careers are around making things more or less recognizable depending on color.
What study makes you think it's insignificant for human faces?
The linked study on cars is analogous at best, but doesn't prove anything for human facial recognition. Cars on highways and human beings in various settings are extremely different circumstances.
And even if you get enough photons to, for example, recognize both black and white equally well in daylight, you might not at night or in a deep shadow indoors, or up close equally well but there will be some distance at which the advantage of more photons begins to matter, or some speed, or some combination of factors.
Any technological improvements will help, but there won't be any technological solution that will always work equally well with fewer photons as with more.
You can't display all of this range to people because of the limitations of display technology, but you can feed the full range to a facial recognition engine.
Security budgets might not stretch to top-end HDR equipment but the price keeps on coming down. The performance of a modern flagship phone is remarkable compared to a few years ago - and fixed surveillance cameras can have much bigger glass and sensors, making it cheaper to get super-human performance.
One new but related issue is that body-worn cameras can capture more low-light detail than the human eye. Police unions have argued against deploying these sensors, because, they want the evidential record to show what the officer could see - not what a cat could see.
While there isn't evidence that dark-skinned people have harder-to-recognize faces, there is also no evidence to the contrary.
It seems like the null hypothesis in this case, given no additional evidence, is to assume that darker images are indeed harder to recognize.
In the early days of color photography there was an issue with some films and reference images being tuned for the most common subject (light-skinned humans) and as a result if you tried to capture a mix of races you'd get bad results: https://petapixel.com/2015/09/19/heres-a-look-at-how-color-f... This makes sense if you consider how light is (generalizing here) a broad spectrum of hues and a given material is most reflective for specific parts of the spectrum, so if you don't capture much there you'll get a low-contrast image, like stripping the R channel out of an RGB bitmap.
It's of course possible to solve the problem for a wider set of skin tones, and it has been solved, but it takes more work. It's a subject of ongoing discussion/experimentation in film to this day: https://www.konbini.com/en/cinema/insecure-cinematographer-h...
Basically, your team is composed of white dudes who don't see the problem with a ML training set consisting largely of pictures of white dudes.
To prevent this they'd have needed to A) Employ a black person, and B) Listen to said employee's feedback, in order to recognize the problem.
Edit: Also worth pointing out, just using a representative population sampling would still show racial bias, essentially weighting accuracy with respect to population percent. You'd probably need to have equal samplings of pictures of people from all races/genders/disabilities if you wanted equal accuracy across the board. That also includes picture quality and range of picture quality. Doubling up images, or using corporate headshot white dudes and grainy selfie People of Color could still cause issues.
Same logic applies to labelling. That minimum wage contracting firm used to decide who's who in the photos may exhibit racial bias, by virtue of the fact that most people do. If their accuracy in labelling is racially biased then so too will the algorithms that it's based on.
In short: Racist garbage in, racist garbage out.
In other words, ideally the racial and gender distribution of a team would be as inconsequential and unbiased as blood type or handedness, in that the aggregate demographic ratios on your teams would at least match that of the residential population in your area, and ideally that of your broader geographic location.
I'm not doing a good job explaining this clearly, but the simple answer is: more than one. No one wants to be the token hire.
Ok, I don't know about race, but for gender look up the "gender equality paradox". In countries with greater equality rights for women they show less of an interest in STEM subjects.
https://en.wikipedia.org/wiki/Gender-equality_paradox
Like I say I don't know of any similar studies done for race, but it would indicate that you shouldn't necessarily expect outcomes that "would match that of the residential population in your area, and ideally that of your broader geographic location".
In my opinion we should be pushing for equality of opportunity, not equality of outcome (you appear to want the latter).
Realistically, the most that hiring managers (save for huge FAANG institutions) can do is thoroughly ensure that their team isn't inadvertently (or blatantly) racist/sexist in their hiring process and on the job, and to post the job in enough places that a diverse applicant pool will see the posting.
With that said, hand-waving away that there are few to no women or African Americans/Latinos/Native Americans/etc. on the team with an overzealous application of the Equality Paradox is a pretty dangerous mindset to get into. It's essentially passing the buck, and is eerily reminiscent of the claims made by 1950's Southern US Politicians that Blacks were the ones who were self-segregating because they wanted to, not the other way around.
What I'm saying is, the ideal 50/50 gender ratio/representative race may be unrealistic for a myriad of reasons, but if you're a 50-person start-up with 2 women, one of whom is HR, and no black people, I'd take a good, hard look at the company culture that's being fostered, and particularly whether turnover for women and People of Color at your company is worse than average.
I have been involved in hiring people before. There was absolutely nothing racist or sexist in the way we hire. Fact was we go two applicants. Neither were women or minority status. Fact is that the industry is full of white men (even here in Europe).
We should be hiring on ability to do the job and nothing else.
> We should be hiring on ability to do the job and nothing else.
This is exactly my point! Yet there is quite a lot of inadvertent, or even blatant, racism and sexism that happens during the hiring process and on the job.
But more to your point, if these oh-so-competent ML practitioners were doing their jobs right, we wouldn't be having this discussion.
The whole reason diversity is championed in hiring is precisely because a single individual's perspective can only see so far. And if you have a monoculture team who has experienced very similar life circumstances, you end up with the kind of narrow perspective that leads to more racist soap dispensers.
And not to say this happened in your case, but even with that considerable effort, it's still very easy to end up with blind spots in your product that a more diverse team would have caught.
It's the same as hiring for any other level of experience for more routine technical skills. If your team has no experience in this area, they'd need to expend a much greater degree of effort to answer questions that someone who is experienced would already have known the answer to.