I-Corps: Historical Photo Identification with Crowdsourcing and Automated Face Recognition

I-Corps项目更广泛的影响/商业潜力是开发一种产生重大文化和经济价值的技术，包括承认历史上被边缘化群体的贡献。虽然我们最初关注的是拍卖行、评估师和交易商的用户群体，但这项工作也适用于画廊、图书馆、档案馆、博物馆(GLAM)和家谱协会收藏的大量身份不明的照片，它们通常人手不足，主要依赖捐赠者和外部研究人员来识别照片。此外，身份识别工作流程也可以从美国内战时代延伸到其他历史时期。最后，这项工作通过向学者和公众提供的商业平台，在技术、艺术和历史之间建立了新的、跨学科的联系。这个i-Corps项目基于在历史照片中识别未知人物的技术的发展。这是一项由广泛的研究人员完成的具有挑战性的任务，包括记者、历史学家、馆长、系谱学家、档案员、交易商和收藏家。目前，这些研究人员主要依靠人工调查方法，比如翻阅数百页参考书，寻找潜在的匹配对象。基于人工智能的面部识别算法和众包为支持这一任务提供了希望，但必须克服诸如假阳性、偏见和群体思维等关键缺点。这项工作探索了新颖的人类-人工智能协作技术，这些技术有效地和伦理地结合了人类和人工智能的互补优势，以支持历史人物识别。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a technology to generate significant cultural and economic value, including recognizing the contributions of historically marginalized groups. While we initially focus on the user groups of auction houses, appraisers, and dealers, this work can also apply to the vast volume of unidentified photos in the collections of galleries, libraries, archives, and museums (GLAMs) and genealogical societies, which are generally short-staffed and largely rely on donors and outside researchers to identify photos. Further, the identification workflow can also be extended beyond the American Civil War era to other historical time periods. Finally, this work fosters new, interdisciplinary connections between technology, art, and history through a commercial platform made available to both scholars and the general public.This I-Corps project is based on the development of technology identifying unknown people in historical photographs. This is a challenging task performed by a broad range of researchers, including journalists, historians, curators, genealogists, archivists, dealers, and collectors. Currently, these researchers largely rely on manual investigative methods such as paging through hundreds of pages of reference books looking for a potential match. AI-based facial recognition algorithms and crowdsourcing offer promise for supporting this task, but key shortcomings, such as false positives, bias, and groupthink, must be overcome. This work explores novel human-AI collaboration techniques that effectively and ethically combine the complementary strengths of human and artificial intelligence to support historical person identification.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.