Much of the recent academic debate in the philosophy of AI revolves around a deceptively simple question: are AI tools only as good as their datasets? Existing discussions, however, tend to focus on improving the quality or quantity of training data, thereby underestimating a more subtle issue: how do AI agents handle outlier cases? This paper examines the ‘majoritarian drift’ – the tendency of AI agents to privilege positions with the largest training footprints, systematically marginalising underrepresented perspectives and edge cases. I approach this problem through the lens of exceptionalism, understood as a condition in which a phenomenon is sufficiently unusual to demand treatment outside standard frameworks. The paper proceeds in two parts. First, I demonstrate that majoritarian drift in ethical reasoning disproportionately favours utilitarian and aggregative approaches, while disadvantaging particularist, casuistic, and minority-tradition ethics. Second, I analyse analogous distortions in logical reasoning, focusing on how majoritarian drift impairs AI judgment in cases involving exceptions to general rules, with particular attention to legal clauses. The paper argues that exceptionalism reveals a structural limitation in how current AI architectures process normative and logical complexity.
