Recent developments in AI, particularly in affective computing, have brought renewed attention to the question of first‑person authority—the authority subjects ordinarily take themselves to have with respect to their own avowals about their mental states. Emotion Recognition Systems, in particular, are often presented as being able to infer what you are feeling, in some cases better than you do. At the same time, recent work on self-knowledge emphasises that much of our self-knowledge is inferential, technologically mediated, and fallible. Together, these developments give rise to a tension: if AI systems can accurately infer our mental states, might they become accurate enough to override our avowals and thereby undermine our authority? I argue that the question rests on a mistaken but common assumption that first-person authority is grounded in a form of epistemic reliability or superiority that subjects enjoy over their mental states, an assumption often inherited from accounts of self-knowledge. Instead, I develop a capacities-based, non-epistemic, account of first-person authority. On this view, first-person authority is grounded in a subject’s distinctive set of capacities to relate to their mental states—through avowal, endorsement, and related capacities—in ways that are not available to others. Although first-person avowals are often accurate as a matter of contingent fact, that is not what makes them authoritative. The upshot is that AI does not pose a threat to first-person authority simpliciter; rather, it helps reveal the types of self-avowals that are vulnerable to challenge.
