That said, the examples are impressive and I can't wait for this level of understandability to become standard in my calls.
There's about equal male and female speakers, though codecs always have slight perceptual quality biases (in either direction) that depend on the pitch. Oh, and everything here is speech only.
It's true that bias questions often come up in ML context because fundamentally these algorithms do not work without data, but _all_ algorithms are designed by people, and _many_ can involve data in setting their parameters. Both of which can be sources of bias. ML is more known for it, I believe, because the _inductive_ biases are less than in traditional algorithms, and therefore are more keen to adopt biases present in the dataset.
"Pretends" is a problematic choice of words, because it anthropomorphizes the algorithm. It would be more accurate and less misleading to replace "pretends to be" with "approximates". But then it wouldn't serve your goal of (seeming to) establish a categorical difference between this approach and "regular algorithms", because that's what a regular algorithm does too.
I apologize, because the above might sound rude. It's not intended to be.
A generative model guesses what data should be filled in, based on what is present in its own model. This process is totally ignorant of the original (missing) data.
To contrast, a lossy codec works directly with the original data. It chooses what to throw out based on what the algorithm itself can best reproduce during playback. This is why you should never transcode from one lossy codec to another: the holes will no longer line up with the algorithm's hole-filling expectations.
There is a difference between pretending to be the original raw data, and pretending to be whatever data will most likely fit.
In my country, public restroom facilities replaced all the buttons and levers on faucets, towel dispensers, etc. with sensors that detect your hand under the faucet. Black people tell me they aren't able to easily use these restrooms. I was surprised when I heard this, but if you google this, it's apparently a thing.
Why does this happen? After all, the companies that made these products aren't obviously biased against black people (outwardly, anyway). So this sort of mistake must be easy to fall into, even for smart teams in good companies.
The answer ultimately boils down to ignorance. When we make hand detector sensors for faucets, we typically calibrate them with white people in mind. Of course different skin tones have different albedo and different reflectance properties, so sensors are less likely to fire. Some black folks have a workaround where they hold a (white) napkin in their hand to get the faucet to work.
How do we prevent this particular case from happening in the products we build? One approach is to ensure that the development teams for skin sensors have a wide variety of skin types. If the product development team had a black guy for example, he could say "hey, this doesn't work with my skin, we need to tune the threshold." Another approach is to ensure that different skin types are reflected in the data used to fit the skin statistical models we use. Today's push for "ethics in ML" is borne out of this second path as a direct desire to avoid these sorts of problems.
I like this handwashing example because it's immediately apparent to everyone. You don't have to "prioritize DEI programs" to understand the importance of making sure your skin detector works for all skin types. But, teams that already prioritize accessibility, user diversity, etc. are less likely to fall into these traps when conducting their ordinary business.
For this audio codec, I could imagine that voices outside the "standard English dialect" (e.g. thick accents, different voices) might take more bytes to encode the same signal. That would raise bandwidth requirements, worsen latency, and increase data costs for these users. If the codec is designed for a standard American audience, that's less of an issue, but codecs work best when they fit reasonably well for all kinds of human physiology.
Do you think we should keep using that codec, because overall we get a better sound quality across all groups? Do you feel the same as a member of group X?
Your same argument can also be used to degrade the performance for all other groups, so that group X isn't unfairly disadvantaged. Or, it can even be used to argue that the performance for other groups should be degraded to be even worse than group X, to compensate for other factors that disadvantage group X.
This is argumentum ad absurdum, but it goes to show that the issue isn't as black and white as you seem to think it is.
And if their contribution was to the final system, sure, it's the owner's choice what the threshold for acceptable contribution is. In the same way they can set any other benchmark.
> Your same argument can also be used to degrade the performance for all other groups,
The context here was Pareto improvement. You're bringing a different situation.
I suggested that the same argument could be used to say that we should actively degrade performance of the algorithm, in the name of equity. This is absurd, and illustrates that the GP argument is maybe not as strong as it appears.
Changing codecs in practice takes years/decades, so you always have time to stop, think and tweak things.
If improving the codec is a matter of training on dataset A vs dataset B, that’s an easier change.
If the codec has the intention of working best for human voice in general, then it is simply not possible to define sensible subsets of the user base to optimize for. Curating an appropriate training set has therefore technical impact on the performance of the codec. Realistically, I admit that the percentages of speech samples of languages in such a dataset would be according to the relative amount of speakers. This is of course a very fuzzy number with many sources of systematic error (like what counts as one language, do non-native speakers count, which level of proficiency is considered relevant, etc.), and ultimately English is a bit more important since it is de-facto the international lingua franca of this era.
In short, a good training set is important unless one opines that certain subsets of humanity will never ever use the codec, which is equivalent to being blind to the reality that more and more parts of the world are getting access to the internet.
I wouldn't consider this a matter of ethics, and more of a technology limitations or ignorance.