Exploring Gender Bias in Fashion Descriptions in Machine Translation to Korean 


Vol. 15,  No. 2, pp. 102-112, Feb.  2026
https://doi.org/10.3745/TKIPS.2026.15.2.102


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  Abstract

Large Language Models (LLMs) have advanced translation, summarization, and QA, yet often embed social biases that threaten fairness and reliability. This study examines gender bias in machine translation with gender-neutral source languages and Korean as the target. We designed prompts focusing on clothing and colors, analyzing outputs from Google Translate and DeepL. Using Jensen–Shannon Divergence (JSD) and the Unadjusted Concordance Assessment (UCA), we evaluated how gender distributions varied with clothing types and color information. Results reveal consistent associations (e.g., dresses and skirts with female, suits with male) and strong bias effects from colors, especially “pink.” Bias patterns also differed across systems and languages, reflecting dataset and model characteristics. This work provides a framework for measuring gender bias in Korean MT and highlights fashion descriptions as a domain where stereotypes are pronounced, offering a methodology extendable to other contexts.

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[IEEE Style]

Y. Park, M. Cho, K. J. Lee, "Exploring Gender Bias in Fashion Descriptions in Machine Translation to Korean," The Transactions of the Korea Information Processing Society, vol. 15, no. 2, pp. 102-112, 2026. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.102.

[ACM Style]

Yo-Han Park, Min-Seok Cho, and Kong Joo Lee. 2026. Exploring Gender Bias in Fashion Descriptions in Machine Translation to Korean. The Transactions of the Korea Information Processing Society, 15, 2, (2026), 102-112. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.102.

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