  As social issues related to gender bias attract closer scrutiny, accurate
tools to determine the gender profile of large groups become essential. When
explicit data is unavailable, gender is often inferred from names. Current
methods follow a strategy whereby individuals of the group, one by one, are
assigned a gender label or probability based on gender-name correlations
observed in the population at large. We show that this strategy is logically
inconsistent and has practical shortcomings, the most notable of which is the
systematic underestimation of gender bias. We introduce a global inference
strategy that estimates gender composition according to the context of the full
list of names. The tool suffers from no intrinsic methodological effects, is
robust against errors, easily implemented, and computationally light.
