  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.
