Beyond Neutrality: Mapping Two Decades of Research on Machine Translation Bias (2005–2024)

As machine translation (MT) becomes increasingly prevalent in multilingual contexts, growing scholarly attention has been directed toward the latent issue of bias. Such bias not only undermines translation quality and user trust but also reinforces structural inequalities, limiting equitable representation and access. However, existing research remains methodologically fragmented and predominantly technical, often lacking integrative perspectives that bridge disciplinary boundaries. This study employs a bibliometric approach, supplemented by close reading, to systematically review MT bias research published between 2005 and 2024 in the Web of Science Core Collection and Scopus databases, investigating publication patterns, thematic hotspots, and methodological trajectories. The findings reveal that: (1) research in this area has undergone three developmental stages, namely initial exploration, steady accumulation, and rapid growth; (2) technological innovation serves as the core driver, while research themes have diversified, accompanied by increasingly close transnational and cross-sector collaborations; (3) current efforts to address the multi-level challenges of bias in MT, including taxonomy (by source, linguistic form and impacted groups), detection (via benchmark construction and system output evaluation), and mitigation (across data collection, model training, and post-processing stages), exhibit multi-focal and multi-path characteristics. Overall, despite notable advances in theory, methodology, and application, MT bias research still suffers from fragmented standards and weak links to real-world practice, underscoring the need for future work that integrates technical precision with ethical reflection through stronger interdisciplinary collaboration.