RESPONSIBLE USE

Gender posterior: what it is, and what it isn't.

Several Mondonomo models return a gender posterior alongside the name they're analyzing. This page explains how that number is derived, what it is intended for, and the uses it must not be put to.

• What it actually is

  The gender field is the conditional probability P(gender | name, locale) computed directly from MondoGraph — the proportion of recorded bearers of that exact name (in that locale, if supplied) who are documented as male or female.

  It is a statistic about a population of past name bearers. It is not an inference about the person whose name was submitted.

• Intended uses

  The posterior is built for aggregate and tie-breaker tasks where calibrated population statistics are the right tool:

  · Disambiguating duplicate records in CRM/CDP merges (e.g., picking the more likely salutation when two records collide).
  · Population-level analytics — how is our customer base distributed across countries and demographic markers.
  · Localization and culturally-aware fallback (e.g., choosing a default honorific in languages that require gendered grammar).
  · QA and detection of clearly-mislabeled records.

• Not-intended uses

  The posterior is not a personal-data inference and must not be used as one:

  · Individual-level classification of a real person's gender.
  · Inputs to decisions that affect a person's access to a product, service, price, credit, insurance, employment, housing, or healthcare.
  · Any context where the EU AI Act, GDPR Article 22, US ECOA, UK Equality Act, or other regulations restrict automated decisions on protected characteristics.
  · Targeted advertising of regulated categories (alcohol, lending, political content) on an individual basis.

  For these use cases, ask the person directly.

• Opt-out and pilot controls

  Pilot deployments can disable the gender field at the API gateway with a single header, return only the type/language/country fields, or replace the gender posterior with a binary “ambiguous” flag above a configurable confidence threshold. Talk to research@mondonomo.ai if any of these apply.

• Why it ships at all

  Calibrated, auditable population statistics on names are useful and lawful for the tasks above — and pretending the signal doesn't exist would just push customers toward worse substitutes (raw LLM guesses, scraped social profiles, name-only Bayes classifiers without evidence counts). Shipping the statistic with this explainer is the better answer.

Questions: research@mondonomo.ai · Related: data handling · Last updated 2026-05-18.