Libraries are increasingly relying on computational methods, including methods from Artificial Intelligence (AI). This increasing usage raises concerns about the risks of AI that are currently broadly discussed in scientific literature, the media and law-making. In this article we investigate the risks surrounding bias and unfairness in AI usage in classification and automated text analysis within the context of library applications. We describe examples that show how the library community has been aware of such risks for a long time, and how it has developed and deployed countermeasures. We take a closer look at the notion of '(un)fairness' in relation to the notion of 'diversity', and we investigate a formalisation of diversity that models both inclusion and distribution. We argue that many of the unfairness problems of automated content analysis can also be regarded through the lens of diversity and the countermeasures taken to enhance diversity.

翻译：图书馆正日益依赖计算方法，包括人工智能方法。这种日益增长的使用引发了对AI风险的担忧，这些风险目前在科学文献、媒体和立法中被广泛讨论。本文在图书馆应用背景下，研究了AI在分类与自动文本分析中存在的偏见和不公平风险。我们通过实例展示了图书馆界如何长期认识到此类风险，并如何制定和实施应对措施。我们深入考察了“（不）公平”概念与“多样性”概念的关系，并对一种同时涵盖包容性与分布性的多样性形式化模型进行了探究。我们认为，自动内容分析中的许多不公平问题，也可以通过多样性视角以及为增强多样性而采取的应对措施来审视。