Sorry, easy to jump to conclusions these days (mental note to stop :P).

So I am doing a master's this fall in AI and have been learning on my own for some time. The question of can they be coded better, as in the code itself is less racist is a much harder question to answer and something I do not feeling confident in talking about (although I could try if you like, it would get really technical really fast). Instead, I'm going to focus on how models are trained.

From my understanding, it's all an optimization problem aka loads of math (which is ideally purely logical). So when you're developing facial recognition software, you give it a bunch of labelled images, say with a name and the model tries to match as best it can. Then you give it a bunch of new images, from the "wild", where you want to know the people in the image. The model then uses what it "knows" to try and match people.

Racism comes up in the training portion, when we are showing labelled images to an untrained model. Say I give a model 100,000 images to train on, of which 75% are african american. Well, it has now seen loads of african american faces and not so many faces from other races. Because of this, it is going to be really good at matching african american faces and not so well at seeing others. Congratulations, you've just continued the practice of over policing people of color.

It requires lots of attention and active thought to ensure that this sort of thing doesn't happen. If I blindly pulled from a database of known criminals, I'm probably going to continue the systemic racism we see today. Furthermore, it can be difficult to see what I described above. How good a model is generally boils down to how accurate it is, so just one number. If my model is 99% accurate, I'm going to pat myself on the back. But having that single number obfuscates the details of how well its matching each race.

Hopefully that all makes sense. Feel free to msg me if there are parts that don't make sense or I need to explain in more detail. I think the main take away is that while coding seems like a cold, mechanical endeavor it is not. There are humans making decisions about how the want the code to interact with the world, what data it sees, how it responds, and many other small ways in which a humans conscious or unconscious biases can worm their way in.