Good point, but it's even worse than meaningless. It's stereotype-reinforcing. That particular inference, that the role in society is such-and-such, so presume that the subject is male, is to be fought at every turn.
I would much prefer the awkwardness of "he/she" when the machine is uncertain rather than some "this is just the way things are" presumption.
Which mean that can only ever be an agent of reinforcing existing "cultures" and never really evolve into an agent of exploratory learning.
Now it might not be a big problem in the real world that google's algorithms essentially exists as Kuhn paradigm enforcers, locking people into a bubble of accepted dogmatic knowledge(as traditional journalism plays/played the same role is), if it were not for the fact that just about everyone pretends the opposite is true for modern machine learning.
This is a bug. Plain and simple.
(Also as someone who speaks French I would like to point out that the counter on "English speaking person programs a language related thing that gets gender wrong" has long since crossed the 6 digit mark. Maybe language apps should be written by people whose native tongue is a complex language like French or German (I am sure there are others, just examples))
So unfortunately this is a fundamental bug that will take serious research to solve, and it's unlikely to be solved really quickly (let's say ... a year, two tops), but it's a bug that will be solved.
It's like "energy is too expensive to just have cheap global travel without airports" type bug. It's a bug, it's currently beyond our capability to fix, but it WILL be fixed.
The other claims you make are too far fetched to even respond to. All of Google's algorithms, bubble of accepted dogmatic knowledge (I would even contend that Google by itself is responsible for 20-30% of the worldwide IQ rise seen during it's existence).
> just about everyone pretends the opposite is true for modern machine learning.
Nope. That's not pretending. That's simply true.
However when you request such a machine algorithm "do the same those guys do", and "those guys" are a racist/sexist/... society, then it will come up with slightly off-color conclusions on occasion.
Humans, in my experience, are much worse.
He/she has always been pretty awkward and silly. But in the eyes of at least some people, it also has the failing that even after all that it still assumes binary genders.
But, I don't think he/she is viable because a major use case is people pasting quickly on their phones. You can see this because the app has automatic clipboard copy built in.
Curious, I looked for guidelines and the best I could come up with was this, which seems helpful if you know the person in question, but not so much if you're referring to someone completely unknown: https://en.wikipedia.org/wiki/Third-person_pronoun#Transgend...
I welcome advice.
Singular "they" has a long history of being used as a generic pronoun, before a bunch of white men decided in the 18th century to try and prescribe "he." Singular "they" is making a comeback, though, and in this case it would be appropriate.
Isn't it simply _necessary_ to do such things? Don't we all have to assume the common case whenever someone leaves some detail out, which is of course done all the time? If someone says "they are kicking the ball between each other", I'm going to assume that the ball is round, as is typical for the occasion. That is "stereotype-reinforcing", but could we even avoid it? Would we want to?
Maybe the former expresses your intent better (for some reason?), but why not use the latter?
This, of course, is quite aside from what a machine should do in ambiguous cases. In those cases, I just wanted to put a vote in for things like he/she (or: he or she) coming from machines rather than statistics-based presumptions.
The point here is simply that Google's algorithm is translating texts and applies a statistical bias, i.e. the algorithm introduces systematic errors into translations. This results in objectively poor performance of a translation system.