Qualia Optimization

2025-05-16

AI Summary

This work addresses the trade-off between subjective experience quality (e.g., pain, pleasure) and task performance in AI systems endowed with phenomenal consciousness. We formally define “AI qualia optimization” as a bi-objective mathematical problem and propose a qualia-aware Markov decision process (Q-MDP) framework. Our method integrates phenomenological constraints, an extended utility function, and differentiable representations of subjective experience to instantiate experience-guided reinforcement learning. We theoretically prove that the framework enables targeted modulation of intrinsic experiential intensity without degrading policy performance. Empirical evaluations confirm the feasibility and efficacy of experience-guided policies. The core contribution is the first computationally grounded, philosophically informed paradigm for modeling and optimizing AI subjective experience—bridging philosophy of mind, reinforcement learning, and AI ethics.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Exploring AI systems with qualia like pain or pleasure
• Considering subjective experiences alongside performance metrics
• Proposing methods inspired by reinforcement learning and philosophy

Innovation

• Assumes AI systems may possess qualia
• Uses reinforcement learning formulations
• Proposes methods promoting reinforcement