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Managing Bias When Library Collections Become Data

机译：管理偏见库集合成为数据时

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摘要

Developments in AI research have dramatically changed what we can do with data and how we can learn from data. At the same time, implementations of AI amplify the prejudices in data often framed as ‘data bias’ and ‘algorithmic bias.’ Libraries, tasked with deciding what is worth keeping, are inherently discriminatory and yet remain trusted sources of information. As libraries begin to systematically approach their collections as data, will they be able to adopt and adapt the AI-driven tools to traditional practices? ? Drawing on the work of the AI initiative within Stanford Libraries, the Fantastic Futures conference on AI for libraries, archives, and museums, and recent scholarship on data bias and algorithmic bias, this article encourages libraries to engage critically with AI and help shape applications of the technology to reflect the ethos of libraries for the benefit of libraries themselves and the patrons they serve. A brief examination of two core concepts in machine learning, generalization and unstructured data, provides points of comparison to library practices in order to uncover the theoretical assumptions driving the different domains. The comparison also offers a point of entry for libraries to adopt machine learning methods on their own terms.
机译：AI研究的发展大大改变了我们可以处理数据以及我们如何从数据中学习的内容。同时，AI的实现放大了数据中的偏见，通常被诬陷为“数据偏见”和“算法偏见”。图书馆，任务决定值得保存，是固有的歧视性的，但仍然是信任的信息来源。由于图书馆开始系统地接近其集合作为数据，他们将能够采用和调整AI驱动的工具传统实践吗？ ？借鉴斯坦福图书馆内的AI计划的工作，图书馆，档案馆和博物馆的奇妙期货会议，以及最近的数据偏见和算法偏见的奖学金，这篇文章鼓励图书馆与AI批判性和帮助形状应用这项技术以反映图书馆的欧洲知识产权的精神，他们所服务的惠顾。简要检查机器学习，泛化和非结构化数据中的两个核心概念，提供与图书馆实践的比较点，以便揭示驱动不同域的理论假设。比较还提供了图书馆的进入点，以获取自己的机器学习方法。

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