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Evidence record 5457 · automatically gathered

FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation

Quality Estimation (QE) aims to assess machine translation quality without reference translations, but recent studies have shown that existing QE models exhibit systematic gender bias. In particular, they tend to favor masculine realizations in gender-ambiguous contexts and may assign higher scores to gender-misaligned translations even when gender is explicitly specified. To address these issues, we propose FairQE, a multi-agent-based, fairness-aware QE framework that mitigates gender bias in b

Record details

Published: 23 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (23 April 2026), “FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation,” evidence record 5457, https://ethics.ai/record/5457 (originally published by arXiv).

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