Inclusive Portraits: Race-Aware Human-in-the-Loop Technology
Inclusive Portraits: Race-Aware Human-in-the-Loop Technology
复制标题
DOI：
10.1145/3617694.3623235
复制
发表时间：
2023-10
期刊：
影响因子：
--
通讯作者：
Claudia Flores-Saviaga;Christopher Curtis;Saiph Savage
中科院分区：
文献类型：
--
作者：
Claudia Flores-Saviaga;Christopher Curtis;Saiph Savage
人工智能彻底改变了各种服务的处理方式，包括对人的自动面部验证。自动化方法已经证明了它们在验证大量人脸方面的速度和效率，但在处理某些社区（包括有色人种社区）的内容时，它们可能会面临挑战。这一挑战促使人们采用了“人在环”（HITL）方法，即人类工作者与人工智能合作，以最大限度地减少错误。然而，大多数HITL方法没有考虑工人的个人特征和背景。本文提出了一种新的方法，称为包容性肖像（IP），它与围绕种族的社会理论联系起来，设计了一个具有种族意识的人在循环系统。我们的实验提供的证据表明，将种族纳入人类在环（HITL）系统中进行面部验证可以显著提高性能，特别是在向有色人种提供服务时。我们的研究结果还强调了在设计HITL系统时考虑个体工人特征的重要性，而不是将工人视为一个同质群体。我们的研究对开发更具包容性和公平性的人工智能增强服务具有重要的设计意义。
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.