Our Data

An open evaluation of name-to-gender inference

We evaluate Genderize against public datasets in which each person's gender is already recorded, reporting accuracy and coverage for every set. Each individual prediction — correct, incorrect, or no prediction — is listed below, so the aggregate figures can be checked and reproduced.

Results

Per-dataset accuracy, and every prediction behind it

Every prediction sits next to the person's real gender, so you can check the numbers yourself.

Choose a dataset 2

Public datasets with a recorded gender for every person, scored with the person's country known.

More datasets added over time.

Showing 26–50 of 88,153

Name	Country	True gender	Predicted	Probability	Result
Aaron Duane Olson	New Zealand	male	male	100.0%	Correct
Aaron Dupnai	Papua New Guinea	male	male	100.0%	Correct
Aaron E. Pollock	United States	male	male	99.8%	Correct
Aaron Egbele	Nigeria	male	male	94.1%	Correct
Aaron Feltham	Canada	male	male	99.9%	Correct
Aaron Fernandes	Canada	male	male	99.8%	Correct
Aaron Gate	New Zealand	male	male	100.0%	Correct
Aaron J. "AJ" Bear	Australia	male	male	100.0%	Correct
Aaron James Ramsey	United Kingdom	male	male	99.8%	Correct
Aaron James Scott	New Zealand	male	male	100.0%	Correct
Aaron John McIntosh	New Zealand	male	male	100.0%	Correct
Aaron John Royle	Australia	male	male	100.0%	Correct
Aaron Kenneth Myette	Canada	male	male	99.8%	Correct
Aaron Lowe	Canada	male	male	99.8%	Correct
Aaron March	Italy	male	male	98.4%	Correct
Aaron Michael Miller	United States	male	male	99.8%	Correct
Aaron Nguimbat	Cameroon	male	male	99.7%	Correct
Aaron Nigel Armstrong	Trinidad & Tobago	male	male	100.0%	Correct
Aaron Parchem	United States	male	male	99.8%	Correct
Aaron Ramirez	United States	male	male	99.9%	Correct
Aaron Russell	United States	male	male	99.8%	Correct
Aaron Teboho Mokoena	South Africa	male	male	98.8%	Correct
Aaron Wells Peirsol	United States	male	male	99.9%	Correct
Aaron Younger	Australia	male	male	100.0%	Correct
Aart Vierhouten	Netherlands	male	male	99.4%	Correct

Method

How we measured this

Population. We score the "alive-today" set: people with a known birth year, at most 80 years old.

Accuracy is the share of returned predictions that matched the known gender. Coverage is the share of people we returned any prediction for — we abstain when there's too little signal, and those show as "no prediction".

Country context. Every prediction here is made with the person's country known — the mode most API users run, and the most accurate one.

Rounding. Figures are the exact measured value to two decimals — we never round accuracy up.