AI has revolutionized the processing of various services, including the
automatic facial verification of people. Automated approaches have demonstrated
their speed and efficiency in verifying a large volume of faces, but they can
face challenges when processing content from certain communities, including
communities of people of color. This challenge has prompted the adoption of
"human-in-the-loop" (HITL) approaches, where human workers collaborate with the
AI to minimize errors. However, most HITL approaches do not consider workers'
individual characteristics and backgrounds. This paper proposes a new approach,
called Inclusive Portraits (IP), that connects with social theories around race
to design a racially-aware human-in-the-loop system. Our experiments have
provided evidence that incorporating race into human-in-the-loop (HITL) systems
for facial verification can significantly enhance performance, especially for
services delivered to people of color. Our findings also highlight the
importance of considering individual worker characteristics in the design of
HITL systems, rather than treating workers as a homogenous group. Our research
has significant design implications for developing AI-enhanced services that
are more inclusive and equitable.

通过社会理论中有关种族的连接，本文提出了一种名为包容人像（Inclusive Portraits）的新方法，改善了人工参与（HITL）系统在面部验证中的性能，并突出了在设计 HITL 系统时考虑个体工作者特点的重要性，而不是将工作者视为同质群体。此研究对于开发更具包容性和公平性的 AI 增强服务具有重大的设计意义。

包容性画像：有关种族的人机交互技术

Inclusive Portraits: Race-Aware Human-in-the-Loop Technology

The rise of concern around Natural Language Processing (NLP) technologies
containing and perpetuating social biases has led to a rich and rapidly growing
area of research. Gender bias is one of the central biases being analyzed, but
to date there is no comprehensive analysis of how "gender" is theorized in the
field. We survey nearly 200 articles concerning gender bias in NLP to discover
how the field conceptualizes gender both explicitly (e.g. through definitions
of terms) and implicitly (e.g. through how gender is operationalized in
practice). In order to get a better idea of emerging trajectories of thought,
we split these articles into two sections by time.
We find that the majority of the articles do not make their theorization of
gender explicit, even if they clearly define "bias." Almost none use a model of
gender that is intersectional or inclusive of nonbinary genders; and many
conflate sex characteristics, social gender, and linguistic gender in ways that
disregard the existence and experience of trans, nonbinary, and intersex
people. There is an increase between the two time-sections in statements
acknowledging that gender is a complicated reality, however, very few articles
manage to put this acknowledgment into practice. In addition to analyzing these
findings, we provide specific recommendations to facilitate interdisciplinary
work, and to incorporate theory and methodology from Gender Studies. Our hope
is that this will produce more inclusive gender bias research in NLP.

该研究回顾了近 200 篇关于 NLP 中有关性别偏见的文章，发现大部分文章没有明确说明他们如何理解性别，甚至没有使用包容性的模型，对非二元性别人群的存在和经验忽视，提出建议以便在 NLP 研究中融入女性学理论和方法，以期实现更加包容的性别偏见研究。