Misgendering is the act of referring to someone in a way that does not
reflect their gender identity. Translation systems, including foundation models
capable of translation, can produce errors that result in misgendering harms.
To measure the extent of such potential harms when translating into and out of
English, we introduce a dataset, MiTTenS, covering 26 languages from a variety
of language families and scripts, including several traditionally
underpresented in digital resources. The dataset is constructed with
handcrafted passages that target known failure patterns, longer synthetically
generated passages, and natural passages sourced from multiple domains. We
demonstrate the usefulness of the dataset by evaluating both dedicated neural
machine translation systems and foundation models, and show that all systems
exhibit errors resulting in misgendering harms, even in high resource
languages.

测量将人的性别识别翻译错误地再现的潜在伤害，并评估翻译系统的表现。

MiTTenS: 评估翻译中的误用性别识别数据集

MiTTenS: A Dataset for Evaluating Misgendering in Translation

Advances in algorithmic fairness have largely omitted sexual orientation and
gender identity. We explore queer concerns in privacy, censorship, language,
online safety, health, and employment to study the positive and negative
effects of artificial intelligence on queer communities. These issues
underscore the need for new directions in fairness research that take into
account a multiplicity of considerations, from privacy preservation, context
sensitivity and process fairness, to an awareness of sociotechnical impact and
the increasingly important role of inclusive and participatory research
processes. Most current approaches for algorithmic fairness assume that the
target characteristics for fairness--frequently, race and legal gender--can be
observed or recorded. Sexual orientation and gender identity are prototypical
instances of unobserved characteristics, which are frequently missing, unknown
or fundamentally unmeasurable. This paper highlights the importance of
developing new approaches for algorithmic fairness that break away from the
prevailing assumption of observed characteristics.

本文探讨了算法公平性在关注性取向和性别认同方面的不足，研究了人工智能对酷儿社群的正面和负面影响，强调了需要开发新的算法公平性方法来解决未被观测到的特征，如性取向和性别认同。