Emotional Cost Functions for AI Safety: Teaching Agents to Feel the Weight of Irreversible Consequences
Humans learn from catastrophic mistakes not through numerical penalties, but through qualitative suffering that reshapes who they are. Current AI safety approaches replicate none of this. Reward shaping captures magnitude, not meaning. Rule-based alignment constrains behaviour, but does not change it. We propose Emotional Cost Functions, a framework in which agents develop Qualitative Suffering States, rich narrative representations of irreversible consequences that persist forward and actively
Record details
Published: 15 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (15 March 2026), “Emotional Cost Functions for AI Safety: Teaching Agents to Feel the Weight of Irreversible Consequences,” evidence record 7209, https://ethics.ai/record/7209 (originally published by arXiv).
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