It's very easy to fix these problems though. There's nothing inherently broken about the models or direction that prevents error rates from being made more uniform. In fact newer facial recognition models with better datasets do perform approximately equally well across skin tones and sex

Easy to fix technically, but first the issue must be recognized and demonstrated, then the delicate process of negotiating the social and economic realities in which the technology operates.

And that's the problem with ML in general: its failure to recognize the implicit biases in choice of dataset and training and the resulting problems, of which Microsoft racist chatbot Tay[1] is merely the most blatantly ludicrous.

1 https://spectrum.ieee.org/in-2016-microsofts-racist-chatbot-...

And the first cars didn't have seatbelts.

It's fine, these are not complicated problems, and they are much easier to spot and fix than most problems in software engineering at scale. Don't be fooled by the negative PR campaigns and clickbait, there's no reason to be skeptical about ML in general because of this.

Also, Tay attempted to solve a much harder problem than image classification. It's hard to build a safe hyperloop. It's no longer hard to build a safe microwave oven.

Forgive me, because I’m not an expert in ML. If this is an easy problem to solve why is it still a problem years after it’s so widespread that msm both knows about it and have written continual investigative journalism about it? It’s clearly not cutting edge anymore once it gets to that point and yet it’s still a problem. Why?

It's some work, but not hard to solve technically having been at companies that deal with very similar problems. The main difficulty is less technical and more investment needed vs value + investment is partly outside of the modeling engineers making the system. Part of the improvement can be done by classical computer vision techniques. But mixing classical computer vision techniques with modern ones both feels somewhat like a hack and complicates the system. The other big area though is dataset improvement. Engineers building ml systems and the people collecting and organizing the needed datasets are normally different people with mild connections to each other. For companies that rely mostly on existing datasets and finetune from them, having to add a data curation process is a big pain point. Most companies have immature data curation processes. Many of the popular open source ml datasets have poor racial diversity. The most popular face generation dataset is celebA, full of celebrities (mostly white ones).

Other issue is for many of these systems having a racial bias in the error rate has mild business impact which makes it harder to prioritize in fixing. Last issue the work needed to fix this tends to be less interesting than most of the other work to make the system.

So overall, the main issues are lack of good open source fair datasets with loose licensing, cross organizational need to solve it (engineers can not code up a fair dataset), and business prioritization.

edit: Also solve here is getting accuracy across races to be close not zero. ML models will always have an error rate and if your goal is 0 errors related to racial factors that is extremely hard. Modeling is about making estimates of data not knowing the truth of that data.

Overfitting is also a technically easy problem to solve, but high profile cases in which it's not solved with obvious negative consequences could also lead to investigative journalism.

The short answer to your question is the same as the one to a lot of programming questions: It's not a technical problem, it's a people problem. Just getting the industry to recognize and acknowledge bias took investigative reporting. The prime example really is the situation with social media and targeted advertising algorithms. We still have people, influential people, like Mark Zuckerberg going around saying that ML isn't really a problem, everything's fine, social media isn't playing any role in destabilizing democracy, targeted ads aren't a threat to anyone's safety, and neither of them have anything to do with the breathtaking levels of economic equality we see.

No doubt there are still plenty of other issues with ML that haven't (yet) made it to popular attention, and the people employing it aren't making decisions based on social value or common good, but simply invoking free markets and capitalism as their guiding philosophies.

I'm afraid the problems with ML are less like "whoops. we don't have seatbelts" and more "surely internal combustion engines optimized for power and mass production couldn't cause problems. It's not like there are going to be millions of them crammed together in lines 3 or 4 across crawling around at 10mph every day. Plus, fossil fuels are cheap, plentiful, and really have no downside we know of. Way better than coal at least - much less awful black smoke!"

Isn’t that just human bias seeping through into the data set, so of course the neural net trained on that will show similar biases. The problem here is the human element.

As they say, it's not a technical problem, it's a people problem. But it's not "just" human, it's that the field in general is elevating ML, AI, whatever you want to call it, with hype like "algorithms aren't biases like a human would be", which is technically true, but also trivial. The people creating these systems didn't even consider that they would reflect and even enshrine, with all kind of high-priest-of-technology-blessings, their biases, that's why we got Tay and why things PredPol is terrible. The key is to acknowledge and actively protect against systematic bias, not make a business of it (coughtwitterfacebookcough).

I'm curious how the physics of light is termed racial bias, it's skin-colour bias if anything -- you can be "black" and be lighter skinned than a "white" person, for example -- but surely it's a consequence of how cameras/light works rather than a bias.

Of course if you don't take account of the difficulties that come with using the tool then you might be acting with racial bias, but that's different. Or, all cameras/eyes/visual imaging means are "racist".

Well, if you really want to know, I have done the research and can recommend several other papers in addition to the one linked. The short answer is that it's not "just physics", and choices made by the chemists and technicians at Kodak, Fuji, Ilford, Agfa, etc to decide how films depicted skin tones were made with racial bias. Digital imaging built on the color rendering tools and tests that originated in the film industry, and thus inherited their flaws.

Sure, that would be interesting to read about - it would be weird not to adjust your film sensitivity according to market, of that were possible. Always happy to learn, link me up.