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.

翻译：人工智能已彻底变革了包括人脸自动验证在内的多种服务流程。自动化方法在大规模人脸验证中展现出速度与效率优势，但在处理特定社群（包括有色人种社群）的内容时可能面临挑战。此问题催生了"人在回路中"（HITL）方法的采用——由人类工作者与AI协同纠错。然而，现有HITL方法鲜少考虑工作者的个体特征与背景。本文提出一种名为"包容性肖像"（IP）的新方法，将种族相关的社会理论融入设计，构建具有种族感知能力的人机协作系统。实验证明，在人脸验证的人机协作系统中融入种族因素，能显著提升性能，尤其对面向有色人种的服务效果突出。研究结果还揭示了在HITL系统设计中重视工作者个体特征（而非将其视为同质群体）的重要性。本研究对开发更具包容性与公平性的AI增强服务具有重要设计启示。