Affective AI Safety: The Missing Piece in LLM Safety
2026-06-22 • Computers and Society
Computers and Society
AI summaryⓘ
The authors point out that most AI safety work looks at factual errors or system failures but doesn't fully consider how AI affects human emotions. They introduce "affective safety" as a way to focus on emotional harms caused by AI, like feeling disconnected from oneself, unfair treatment, or damaged relationships. They categorize these emotional harms and show that current safety efforts often miss them. The authors suggest new approaches are needed to understand and manage these emotional risks in AI systems.
AI safetyaffective harmsself-alienationfairnessbiasrelational harmsemotional impactAI ethicsregulatory challenges
Authors
Carolin Ifländer, Alba Curry, Flor Miriam Plaza-del-Arco, Amanda Cercas Curry
Abstract
AI safety research has focused predominantly on epistemic and physical harms (e.g., misinformation, bias, system reliability) while the risks that arise from AI systems' engagement with human emotional life have remained fragmented and undertheorised. We propose affective safety as a unified class of AI safety concerns grounded in the fact that humans are affective beings. We develop a taxonomy of affective harms and identify recurring harm types: (1) affective self-alienation, (2) fairness and bias harms, and (3) relational harms. We show that their recurrence across system types reflects structural properties of how AI systems engage with human emotion and survey the current safety landscape and show that existing frameworks address affective safety either narrowly or not at all. We conclude by identifying the technical and regulatory challenges specific to this class of harms and argue that affective safety requires dedicated frameworks that engage with cumulative, relational, and identity-level effects.