Triangulating Evidence for Machine Consciousness Claims: A Validity-Centered Stack of Behavioral Batteries, Mechanistic Indicators, Perturbation Tests, and Credence Reporting
Proceedings of the AAAI Symposium Series May 18, 2026 Scott Hughes, Karen Nguyen
Frontier AI systems produce responses that lead people to wonder if they might be conscious. A new framework, the Triangulated Consciousness Assessment Stack (TCAS), combines four evidence streams to distinguish genuine indicators from optimized artifacts or surface-level cues: behavioral batteries with robustness controls, mechanistic indicators with explicit assumptions, perturbation tests probing causal sensitivity and proxy failures, and observer-confound controls separating anthropomorphic attribution from evidence. When all streams are available, TCAS produces theory-indexed credence bands and standardized disclosure cards rather than binary verdicts. An empirical evaluation of GPT-5.2 Pro covered only behavioral and perturbation streams; missing streams meant credence bands were withheld.