本研究旨在探討性別偏見如何影響司法判決結果。我們共使用了860,000筆刑事判決書為個別法官建構性別態度指標Gender Slant，為控制案件類型及法庭類別的異質性，研究在聚焦於家暴案件的同時分開討論簡易庭與非簡易庭的資料，並進一步透過迴歸模型納入法官性別與性別偏見的交互項以及不同時期與性別偏見的交互項，以便檢驗偏見效果是否隨法官性別或法規變化而有所差異。結果顯示，具較高傳統性別偏見的女性法官在各制度與法院類型下皆傾向輕判；男性法官則在非簡易庭的改制後期，性別偏見與重判傾向呈現正向關聯。我們推測此不對稱性源於性別角色的內化觀點，即傳統男性強調養家責任，女性則強調家庭奉獻，當家暴案件中違反性別角色時，法官對自身性別角色的期待會產生更強烈的回應。本文亦為首篇運用詞嵌入技術量化個別法官性別偏見，並系統性分析其對量刑結果之影響的研究。

This study investigates how gender bias affects judicial sentencing outcomes. We employ machine learning on 860,000 criminal verdicts to identify judges’ implicit gender attitudes. To control for case-type heterogeneity, the analysis focuses on domestic violence cases and distinguishes between Summary and Ordinary Courts. The empirical strategy uses regression models with interaction terms between judge gender and gender bias, as well as between gender bias and different institutional periods, to examine whether the effect of gender bias varies with judge gender or institutional reforms. The results show that female judges with stronger traditional gender attitudes tend to impose more lenient sentences across all institutional periods and court types. Male judges, however, in the post-reform period of Ordinary Courts, exhibit a positive association between traditional gender bias and harsher sentencing. We argue that this asymmetry may stem from internalized gender norms: Traditional gender norms portray men as authorities and protectors, and women as supporters and followers. When either party in a domestic violence case violates conventional gender expectations, judges may respond more strongly based on the gender roles they themselves internalize. This paper contributes to the literature as the first study to systematically examine the effect of judicial gender bias on sentencing decisions by quantifying it through word embedding techniques.

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