Second Opinion: Supporting Last-Mile Person Identification with Crowdsourcing and Face Recognition
Second Opinion: Supporting Last-Mile Person Identification with Crowdsourcing and Face Recognition
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DOI：
10.1609/hcomp.v7i1.5272
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
V. Mohanty;Kareem Abdol-Hamid;C. Ebersohl;K. Luther
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文献类型：
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作者：
V. Mohanty;Kareem Abdol-Hamid;C. Ebersohl;K. Luther
随着基于人工智能的人脸识别技术越来越多地应用于定位犯罪嫌疑人等高风险应用，公众对这些技术准确性的担忧也与日俱增。这些技术通常会向人类专家提供一份高置信度候选面孔的候选名单，专家必须从中选择正确的匹配，同时避免误报，我们将其称为“最后一英里问题”。我们提出了 Second Opinion，这是一种基于网络的软件工具，它采用受认知心理学“种子收集分析”启发的新颖的众包工作流程，以协助专家解决最后一英里的问题。我们通过一项混合方法的实验室研究评估了第二意见，该研究涉及 10 名专家和 300 名群众工作者，他们合作识别历史照片中的人物。我们发现，群众可以消除人脸识别建议的最高置信度候选人中 75% 的误报，而且专家们热衷于在工作中使用第二意见。我们还讨论了人群与人工智能交互和众包人员识别的更广泛影响。
As AI-based face recognition technologies are increasingly adopted for high-stakes applications like locating suspected criminals, public concerns about the accuracy of these technologies have grown as well. These technologies often present a human expert with a shortlist of high-confidence candidate faces from which the expert must select correct match(es) while avoiding false positives, which we term the “last-mile problem.” We propose Second Opinion, a web-based software tool that employs a novel crowdsourcing workflow inspired by cognitive psychology, seed-gather-analyze, to assist experts in solving the last-mile problem. We evaluated Second Opinion with a mixed-methods lab study involving 10 experts and 300 crowd workers who collaborate to identify people in historical photos. We found that crowds can eliminate 75% of false positives from the highest-confidence candidates suggested by face recognition, and that experts were enthusiastic about using Second Opinion in their work. We also discuss broader implications for crowd–AI interaction and crowdsourced person identification.