Find answers to our most frequent questions

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Most names are predominantly used for either males or females, so a statistical approach will often yield good results. There are of course names that are used across both sexes, but a statistical approach will reveal that too. Furthermore, naming conventions can rely on cultural differences, so scoping statistics to a single country will help achieve more accurate results.

Gender prediction works by statistical probability and as such it is hard to talk about accuracy. If a certain name yields a female probability of 92%, it means that 92% of our dataset for that name is female. If your own data is unbiased, it will be correct 92% of the time.

For a comparison of gender inference methods you can read Lucía Santamaría and Helena Mihaljević (2018), Comparison and benchmark of name-to-gender inference services .

You'll find tons of scholarly articles making use of Genderize. Here's a few places to get started:

• Papers using Genderize.io on arXiv
• Life science papers using Genderize.io on Europe PMC
• Medical publications using Genderize.io on JAMA Network
• Scientific publications using Genderize.io on Springer
• Scientific publications using Genderize.io on MDPI

Our dataset consists of more than a billion people. You can read about the Genderize dataset to learn more and understand of the coverage is distributed.