Qualia Optimization
Philip S. Thomas
arXiv Preprint Archive May 16, 2025 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Keywords | Cs.ai |
| Key points | Proposes that AI qualia could be integrated into optimization by framing problems mathematically, and suggests methods that promote reinforcement based on these subjective experiences. |
Abstract
This report explores the speculative question: what if current or future AI systems have qualia, such as pain or pleasure? It does so by assuming that AI systems might someday possess qualia -- and that the quality of these subjective experiences should be considered alongside performance metrics. Concrete mathematical problem settings, inspired by reinforcement learning formulations and theories from philosophy of mind, are then proposed and initial approaches and properties are presented. These properties enable refinement of the problem setting, culminating with the proposal of methods that promote reinforcement.