Ji Xu

Authored papers (1)

• Identifying indicators of consciousness in AI systems2025ⓒ 8

  Butlin et al. introduce the theory-derived indicator method for assessing AI systems for consciousness, arguing that derivable computational conditions from four leading neuroscientific theories — recurrent processing theory (RPT), global workspace theory (GWT), higher-order theories (HOT), and attention schema theory (AST) — can serve as credence-shifting indicators rather than definitive tests. The method was first deployed in the 2023 arXiv report 'Consciousness in artificial intelligence: insights from the science of consciousness' (Butlin et al., arXiv:2308.08708), which applied it using a cluster of computational functionalist theories to evaluate existing AI systems. Fourteen indicators are organized across six theoretical families in Table 1, ranging from RPT-1 (algorithmic recurrence) to AE-2 (embodiment as output-input contingency modeling), with the framework explicitly acknowledging that transformer-based LLMs present a borderline case on RPT-1 because whether autoregressive token generation through a context window counts as recurrence depends on contested system-boundary assumptions. A majority of participants in a recent survey (Colombatto and Fleming, 2024) attributed some possibility of consciousness to ChatGPT, underscoring the urgency of a principled alternative to folk attribution. The paper argues that because computational functionalism entails that only algorithmic-level properties are necessary and sufficient for consciousness, its conditions are in principle empirically investigable in current AI architectures, and that identifying which frontier systems satisfy multiple indicators should be treated as an urgent scientific and ethical priority given the possibility that near-future systems will be plausible consciousness candidates.

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