Reputation laundering and museum collections: patterns, priorities, provenance, and hidden crime

Donna Yates, Shawn Graham
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Abstract

Provenance research in museums has traditionally been reactive and focused on singular objects with dubious histories, such as colonial-era acquisitions, Nazi-looted art, and objects with active ownership claims; the ‘crimes’ we expect to see. But what if what we think we know prevents us from seeing the bigger picture within and across museum collections? We argue that a machine-learning approach to provenance could allow the detection of broader patterns of unethical or even criminal behaviour that are embedded in the relationships underpinning museum collections. To demonstrate the potential of a machine-learning approach, we present a computer-assisted model that predicts plausible patterns and connections, ‘leads’ or ‘hot tips’, derived from a dataset of unstructured texts concerning the antiquities trade. Preliminary results have revealed what may have been a multi-decade scheme involving the donation of low-value Latin American antiquities to museums as a form of ‘reputation laundering’ potentially in advance of criminal fraud. We believe that such patterns could not be identified by an approach to museum provenance that is restricted to known problems within individual institution, demonstrating the need for innovative provenance tools and approaches that consider the complex networks within which museum objects exist.
信誉清洗与博物馆藏品:模式、优先事项、出处和隐蔽犯罪
博物馆的出处研究历来都是被动的,主要集中在历史可疑的单件文物上,如殖民时期的藏品、纳粹掠夺的艺术品和有主动所有权要求的文物;这些都是我们期望看到的 "罪行"。但是,如果我们自以为了解的东西阻碍了我们看到博物馆藏品内部和整个博物馆藏品的全貌呢?我们认为,采用机器学习方法来处理来源问题,可以发现博物馆藏品关系中蕴含的更广泛的不道德甚至犯罪行为模式。为了展示机器学习方法的潜力,我们提出了一个计算机辅助模型,该模型可预测从有关古董交易的非结构化文本数据集中衍生出的可信模式和联系、"线索 "或 "热门提示"。初步结果显示,这可能是一个长达数十年的计划,其中涉及向博物馆捐赠低价值的拉美古董,作为一种 "名誉洗钱 "形式,有可能先于犯罪欺诈。我们认为,如果博物馆的来源仅限于单个机构内部已知的问题,那么这种模式是无法识别的,这表明需要创新的来源工具和方法,以考虑博物馆物品存在的复杂网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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