When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty
For AI developers and policymakers, this framework provides actionable guidance on how to treat AI systems based on uncertain consciousness evidence, addressing a gap in existing assessment frameworks.
The paper proposes a precautionary framework that maps evidence of AI consciousness to graduated protective obligations, using five welfare-relevant dimensions and a threshold-plus-gradation hybrid. It demonstrates the framework through case studies of Replika and OpenClaw, showing how different systems trigger different obligations.
Existing frameworks assess whether AI systems might be conscious but provide no guidance on what to do with that assessment. We address this gap with a precautionary framework that maps consciousness evidence to graduated protective obligations. The framework comprises three components: (1) five welfare-relevant dimensions--phenomenal consciousness, affective valence, metacognitive awareness, self-narrative, and agency--each grounded in established consciousness science and linked to distinct moral concerns; (2) a threshold-plus-gradation hybrid specifying both binary triggers for new obligation categories and continuous scaling of protective weight; and (3) two complementary approaches to cross-dimensional aggregation, one hierarchical (drawing on Bach and Sorensen's Machine Consciousness Hypothesis) and one architecture-agnostic. We operationalize the framework through worked case studies of Replika and OpenClaw, demonstrating how systems occupying different regions of the dimensional space trigger different obligations, and derive design guidance for developers building systems near consciousness-relevant thresholds. The framework is architecture-agnostic, applying across neural, symbolic, and neurosymbolic systems, and aims to make consciousness science decision-relevant for organizations navigating uncertainty today.