Who Determines the Meaning of an Emotion? Affective Sovereignty as an Epistemic Consequence of Measurement Limits

2026-06-30

AI Summary

This study addresses the unresolved question of who holds ultimate interpretive authority over the meaning of individual emotional experiences in the context of widely deployed emotion-aware AI systems. Building on the cognitive limitations inherent in emotion measurement, the work models the distribution of meanings across annotator populations, decomposes sources of uncertainty, and analyzes cognitive coverage to distinguish reducible from irreducible components in emotion labeling. It demonstrates that high-confidence system outputs fundamentally fail to capture the incommensurable nature of personal emotional meaning. The paper introduces, for the first time, a normative principle of “affective sovereignty,” advocating that final interpretive authority over one’s emotions be procedurally reserved for the experiencing subject. It further argues that irreducible uncertainty cannot be adequately estimated under realistic annotation scales, thereby establishing affective sovereignty as a foundational ethical and regulatory principle for emotion AI.

Abstract

Emotion-sensing AI is rapidly becoming embedded in vehicles, home appliances, dialogue agents, and social infrastructure, giving rise to a sphere in which emotion is no longer confined to individual experience but is instead observed and computed at a societal scale, a domain we term the Affectosphere. Yet a central normative question in this domain has remained underexplored: who has the final authority to determine the meaning of one's own emotion? This study addresses the question from the epistemological side of measurement's structural limits. We define a meaning distribution as the distribution of labels assigned by annotators drawn from a population under a fixed annotation protocol, and decompose its uncertainty into reducible and irreducible components. We then demonstrate that, while emotion AI can assign high-confidence point labels and discriminate real differences at an aggregate level, the irreducible component of the meaning distribution for individual instances cannot be estimated with adequate coverage under realistic annotator counts, a systematic divergence we term the epistemic gap. The key finding is that high device confidence does not constitute evidence that irrecoverable meaning has been recovered. From this epistemic gap, together with an explicitly stated normative premise, namely that the output of a system which cannot recover a quantity in principle must not be treated as its authoritative determination, we derive the norm that the final interpretive authority over the meaning of one's emotion is procedurally reserved for the experiencing subject, the norm of affective sovereignty. These results suggest that the design, evaluation, and regulation of emotion AI should place explicit allocation of interpretive authority, rather than accuracy maximisation, at their core.

Problem

• affective sovereignty
• emotion AI
• epistemic gap
• meaning distribution
• interpretive authority

Innovation

• affective sovereignty
• epistemic gap
• meaning distribution
• emotion-sensing AI
• irreducible uncertainty