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AI has revolutionized the processing of various services, including theautomatic facial verification of people. Automated approaches have demonstratedtheir speed and efficiency in verifying a large volume of faces, but they canface challenges when processing content from certain communities, includingcommunities of people of color. This challenge has prompted the adoption of"human-in-the-loop" (HITL) approaches, where human workers collaborate with theAI 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 raceto design a racially-aware human-in-the-loop system. Our experiments haveprovided evidence that incorporating race into human-in-the-loop (HITL) systemsfor facial verification can significantly enhance performance, especially forservices delivered to people of color. Our findings also highlight theimportance of considering individual worker characteristics in the design ofHITL systems, rather than treating workers as a homogenous group. Our researchhas significant design implications for developing AI-enhanced services thatare more inclusive and equitable.

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