Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Terms

• URL: http://arxiv.org/abs/2503.04372v2
• Date: Tue, 20 May 2025 09:17:40 GMT
• Title: Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Terms
• Authors: Orfeas Menis Mastromichalakis, Giorgos Filandrianos, Maria Symeonaki, Giorgos Stamou,
• Abstract summary: We introduce GRAPE, a probability-based metric designed to evaluate gender bias.<n>We present GAMBIT-MT, a benchmarking dataset in English with gender-ambiguous occupational terms.
• Score: 2.5764960393034615
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine Translation (MT) systems frequently encounter gender-ambiguous occupational terms, where they must assign gender without explicit contextual cues. While individual translations in such cases may not be inherently biased, systematic patterns-such as consistently translating certain professions with specific genders-can emerge, reflecting and perpetuating societal stereotypes. This ambiguity challenges traditional instance-level single-answer evaluation approaches, as no single gold standard translation exists. To address this, we introduce GRAPE, a probability-based metric designed to evaluate gender bias by analyzing aggregated model responses. Alongside this, we present GAMBIT-MT, a benchmarking dataset in English with gender-ambiguous occupational terms. Using GRAPE, we evaluate several MT systems and examine whether their gendered translations in Greek and French align with or diverge from societal stereotypes, real-world occupational gender distributions, and normative standards.

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