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Comment by joaomsa | Hacker News Reader

One use I've come across for inferring genders was tackling record depupliction across multiple data sources. In one data set we might have the gender information of an individual but have it be missing in another.

Turns out gender is great to include in a blocking keys to reduce number of comparisons. Extrapolating an inferred gender in the dataset without one was incredibly helpful.

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You are forgetting that the individual might consider the gender to be private information. Some people might want to use a different gender in different contexts. See the ESPN/Grantland suicide issue recently if you care/dare.

An individual that considers gender to be private information , and that uses different genders in different situations is very unlikely to be using a name that can be classified with a high degree of confidence as one gender.

A person using the name "Jack" is unlikely to be assumed to be a woman, even if that person selected "female" from a drop down somewhere. If the same person uses Jack/M and Cindy/F in different contexts, no fuzzy algorithm is going to resolve them as the same person (bar some other, stronger ID, such as a SSN).

EDIT: I initially used "William" as an example. Ironically, it turns out that name is only 57.6% male. Both Jack and Cindy are 90% male/female.

At least in the UK, Jack is a fairly common shortening of Jacqueline. The only way of determining a users gender is asking them directly, and if a person wishes to enter different values into different systems, all the more power to them.

"I initially used "William" as an example. Ironically, it turns out that name is only 57.6% male. Both Jack and Cindy are 90% male/female."

You have just used the numbers from the API as evidence of its own accuracy.

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