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this post was submitted on 31 Aug 2023
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WEIRD bias strikes again.
A stealth bomber gives less signal because of angles and materials and how they interact with radar, not because they are small or painted a dark color.
If a dark skinned person and a white skinned person are both wearing the same pants and long sleeved shirts, why would skin color be a factor beyond some kind of poorly implemented face recognition software like auto focus on cameras that also don't work well for dark skinned folks? Especially when some of the object recognition is just looking for things in the way, not necessarily people.
No, it is not some simple explanation based on people's eyes from the driver's seat while driving in the dark. It is a result of the systems being trained based on white adults (probably men based on most medical and tech trials) instead of being trained on a comprehensive data set that represents the actual population.
While some of these cars use radar to an extent. I believe this is mostly focusing on image recognition, which is from a camera. Both are distinctly different in how they recognize objects.
Image recognition relies on cameras which relies on contrast. All of which is dependent on light levels. One thing to note about contrast is that it’s relative to its surroundings. I think this situation is more similar to your eyes recognizing things while driving in the dark than you think. I suggest you research how these things work before making claims.
So white people have higher contrast than dark skinned people?
It's both. The system is racist because of how it was trained and because its developers were not black, therefore "it worked for them" during development. And because black people are harder for cameras to see, especially in low light environments.
Even with clothes on, the dark skin, in a dark environment, "breaks" the "this is human" pattern that the ai expects to see, since the ai can see only the clothes. It is like camouflage. Can the ai "see" a pair of pants? Maybe, eventually but it still reduces the certainty, since the ai sees fewer "signs".
Cameras should be using infrared to look for objects in the dark and not fucking hoping it looks slightly less dark than the surrounding pixels. It being “dark” is not an excuse. Cars drive at night and need to be engineered around that fact.
Edit: note this is about cameras. Ideally, you’d use radar which wouldn’t care but if you are just dual purposing cameras used for driving, this is the bare minimum.
These systems are often trained on data obtained from driving the car around. I think the only real solution would be planning routes through more diverse neighborhoods. Although any company that is taking this seriously from a safety perspective has multiple radars and a top mounted LiDAR on their vehicles. Those sensors should be sufficient for detecting humans regardless of race even in a completely dark environment. Relying solely on camera data is just asking for problems for this and many other reasons.
Except that’s not the source of this problem. AI can be great at detecting patterns with little data, if it’s properly trained. But this article is clear that the reason of this failure is in the lack of training data. This means that the AI never learned kids and dark-skinned people exist and it’s unreliable in detecting them.
I’m sure that will be of great comfort to any dark-skinned person or child that gets hit.
If those are known, expected issues? Then they had better program around it before putting driverless cars out on the road where dark-skinned people and children are not theoreticals but realities.
In order to make the software detect the same you have to make it detect white adult less.
Comparing the performance between races says nothing about how safe a driverless car is. I am sure that the chances of a human hitting a dark skinned person dwarfs the chances of a driverless car. Trying to convince people driverless cars are racist only delays development, adoption and lawmaking which means more flawed meatbags behind the wheel which means more car accident deaths.
What he's saying is these aren't issues, they're like saying a masculine voice can be heard from further away. Deeper voices just carry better
Part of it is bias/training data - we can fix that. But then you're still left with the fact children are smaller and dark skinned people are darker - if you use the human visible range of light (which most cameras do), they're always going to be harder to detect than larger more reflective people.
Our eyes and brains have an insane ability to focus and deal with varying levels of light, literally each cell adapts individually to each wavelength. We don't have much issue picking out anyone until it becomes extremely dark or extremely far away - it's not because the problem is easy, it's because humans are incredible at it
Thank you.
You seem to be one of the people who understand this better.
And even humans are not incredible at it. It's just inherently harder to identify the areas where there are less signal. I'd love to see a study, but see my edit and actually quantifying the equality we're after.
Reality/physics/science/PDEs (whatever) work on "differences". The less difference, the harder.
Yes but isn’t it easier to say RACISM
You sound like an imaging specialist with no experience
That's only part of it though. This issue is almost as old as we have had similar image/facial recognition technologies. Data is where models get their conclusions from.
Wow. that’s all kinds of incorrect
It is absolutely data training bias. Whether it is the data that ML was trained on or the data that programmers were trained on is irrelevant. This is a problem of the computer not recognizing that a human is a human
It is not. See below:
No, not if the scale of your hardware is correct. A 3’ tall human may be smaller than a 6’ one, but it is larger than a 10” traffic light lens or a 30” stop sign. The systems do not have quite as much trouble recognizing those smaller objects. This is a problem of the computer not recognizing that the human is a human.
Also no. If that were the case, then we would have problems with collision bias against darker vehicles, or not being able to recognize the black asphalt of the road. Optical systems do not rely on the absolute signal strength of an object. they rely on contrast. A darker skin tone would only have low contrast against a background with a similar shade, and that doesn’t even account for clothing which usually covers most of a persons body. Again, this is a problem of the computer not recognizing that the human is a human.
No, they have different signals. that signal needs to be compared to the background to determine whether it exists and where it is, and then compared to the dataset to determine what it is. This is still a problem of the computer not recognizing that the human is a human.
Close, but not quite.
This is a problem of the computer not recognizing that the human is a human.
You don’t know that.
Speaking as someone who inherited a computer vision codebase from Asian devs and quickly found that it didn’t work on white skin…
Implementation decisions matter, and those decisions will always be biased towards demonstrating successful output for the people who plan, bankroll, and labor on the project.
How much of the 20% or 7.5% difference in detection is due purely to inevitable drawbacks of size and skin tone?
Who knows.
What we do know is that we did measure a difference, and we do live in a culture where we’re more likely to hear a CEO say:
“It works!” …and then see an article months later that adds “…except for children and black people.”
rather than:
“It doesn’t work!” …and then see an article months later that adds “…except for adults and white people.”
And that fact means we should seriously consider whether our attention (and intention) is being fairly applied here.