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 reshape character. Unlike numerical penalties, qualitative suffering states capture the meaning of what was lost, the specific void it creates, and how it changes the agent's relationship to simila...
| Authors | Pandurang Mopgar |
|---|---|
| Primary category | cs.AI |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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