Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission (EC), the fifth requirement ("Diversity, non-discrimination and fairness") declares: "In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system's life cycle. [...] This requirement is closely linked with the principle of fairness". In this paper, we try to shed light on how closely these two distinct concepts, diversity and fairness, may be treated by focusing on information access systems and ranking literature. These concepts should not be used interchangeably because they do represent two different values, but what we argue is that they also cannot be considered totally unrelated or divergent. Having diversity does not imply fairness, but fostering diversity can effectively lead to fair outcomes, an intuition behind several methods proposed to mitigate the disparate impact of information access systems, i.e. recommender systems and search engines.

翻译：在欧盟委员会（EC）设立的人工智能高级别专家组（AI-HLEG）提出的实现可信人工智能的七项关键要求中，第五项要求（"多样性、非歧视与公平性"）指出："为实现可信人工智能，我们必须在整个人工智能系统的生命周期中促进包容性和多样性。[……]这一要求与公平性原则密切相关。"本文试图通过聚焦信息访问系统与排序相关文献，阐明多样性与公平性这两个不同概念可能被探讨的紧密关联。这两个概念不应被互换使用，因为它们代表着不同的价值，但我们主张它们也不能被视为完全无关或相互分歧的。具备多样性并不意味着公平，但促进多样性可以有效引导出公平的结果——这一直觉支撑了为缓解信息访问系统（即推荐系统和搜索引擎）差异性影响而提出的多种方法。