Revisiting Classic Thought Experiments to Measure Consciousness for Artificial Intelligence Safety

Behavior alone is insufficient to determine machine consciousness: The paper demonstrates that two AI systems can achieve identical task performance ($W_{causal,T}$) while having fundamentally different internal organizations—one using a simple lookup table and another using a compact generative model—suggesting that passing behavioral tests (like the Turing test) doesn't reveal what's actually happening inside the system.

Internal efficiency matters for safety analysis: The paper introduces "operational consciousness" ($κ_T$) as a measure of how efficiently a system reuses its internal structure. Systems that wastefully store unused mappings versus those that compress knowledge into reusable patterns represent different safety considerations, even when their outputs are identical.

Classic philosophical thought experiments need updating: By formalizing Leibniz's mill, Turing's imitation game, and Searle's Chinese Room through a modern framework, the paper shows these centuries-old debates about understanding and consciousness remain relevant to AI safety—but require precision about what we're actually measuring (behavior vs. internal organization).

The distinction between "doing" and "understanding" has practical implications: The paper reframes the classic philosophical dispute about whether machines can truly "understand" by separating outward performance from the internal mechanisms supporting it, suggesting this separation is crucial for developing responsible AI safety practices.