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This is an important question. However, I'd like to point out that similar biases can easily exist for non-ML, hand-tuned algorithms. Even in the latter case te
by radarsat1 3y ago
This is an important question. However, I'd like to point out that similar biases can easily exist for non-ML, hand-tuned algorithms. Even in the latter case test sets and often even "training" and "validation" sets are used for finding good parameters. Any of these can be a source of bias, as can the ears of evaluators making these decisions.
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.
- thomastjeffery 3y agoUsually regular algorithms aren't generating data that pretends to be raw data. That's the significant difference here.
- shwaj 3y agoCan you precisely define what you mean by "generating" and "pretends", in such a way that this neural network does both these things, but a conventional modern audio codec doesn't? "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.
- thomastjeffery 3y agoI was avoiding the word "approximate", because that implies a connection to the original raw data. 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.
- deleted 3y ago[deleted]
- samus 3y agoNot really. Any lossy codec is generating data that pretends to be close to the raw data.
- thomastjeffery 3y agoYes, but the holes were intentionally constructed such that the end result is predictable. There is a difference between pretending to be the original raw data, and pretending to be whatever data will most likely fit.
- samus 3y agoAnd that's why Packet Loss Concealment is only used to fill in the occasional lost packet. That way, the occasional vowel or the end of a syllable could be bridged over. Other improvements exist to prevent packet loss in the first place that are much less in make-samples-up territory.
- thomastjeffery 3y agoYes, I agree that this use case is entirely reasonable. I also agree that it's reasonable to be concerned in the first place.
- MauranKilom 3y agoAs a notable example, the MP3 format was hand-tuned to vocals based on "Tom's Diner" (i.e. a female voice). It has been accused of being biased towards female vocals as a result.