Recommendation systems (RSs) predict what the user likes and recommend it to them. While at the onset of RSs, the latter was designed to maximize the recommendation accuracy (i.e., accuracy was their only goal), nowadays many RSs models include diversity in recommendations (which thus is a further goal of RSs). In the computer science community, the introduction of diversity in RSs is justified mainly through economic reasons: diversity increases user satisfaction and, in niche markets, profits.I contend that, first, the economic (...) justification of diversity in RSs risks reducing it to an empirical matter of preference; second, diversity is ethically relevant as it supports two autonomy rights of the user: the right to an open present and the right to be treated as an individual. So far, diversity in RSs has been morally defended only in the case of RSs of news and scholarly content: diversity is held to have a depolarizing effect in a democratic society and the scientific community and make the users more autonomous in their news choices. I provide a justification of diversity in RSs that embraces all kinds of RSs (i.e., a holistic moral defense) and is based on a normative principle founded on the agency of the user, which I call the right to be an exception to predictions. Such a right holds that the proper treatment of a RS user qua agent forbids providing them with recommendations based only on their past or similar users’ choices. (shrink)