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This paper proposes a causal framework to evaluate claims of artificial consciousness by distinguishing between behavioral similarities and the underlying causal mechanisms required for subjective experience. It argues that current AI systems offer limited evidence for consciousness because their internal causal structures differ fundamentally from biological systems, despite surface-level cognitive parallels. For TechPulse readers, this research clarifies the rigorous evidentiary standards needed before asserting machine sentience, shifting the debate from output mimicry to architectural correspondence.
What Can Analogy Tell Us About Artificial Consciousness?
arXiv:2610.01002v1 Announce Type: new Abstract: Who or what is conscious? Because subjective experience is directly accessible only in the first person, judgments about consciousness in other entities depend partly on analogy. Historically, such inferences have focused on nonhuman animals, but advances in artificial intelligence have raised the possibility of conscious AI. Here we develop a causal framework for evaluating such evidential analogies. The key distinction is between similarities in factors plausibly involved in generating consciousness and similarities in downstream behavioural or cognitive effects. Our framework weights source-target similarity by causal relevance while allowing for unknown causes, disabling differences and alternative routes to consciousness. Applied to biological systems, it explains why analogical support generally weakens with increasing causal distance from humans. Applied to contemporary AI, it suggests that behavioural similarity provides only limited evidence for consciousness because relevant causal correspondences remain poorly established. The framework also clarifies what evidence would strengthen claims of artificial consciousness.
Title:What Can Analogy Tell Us About Artificial Consciousness?
Abstract:Who or what is conscious? Because subjective experience is directly accessible only in the first person, judgments about consciousness in other entities depend partly on analogy. Historically, such inferences have focused on nonhuman animals, but advances in artificial intelligence have raised the possibility of conscious AI. Here we develop a causal framework for evaluating such evidential analogies. The key distinction is between similarities in factors plausibly involved in generating consciousness and similarities in downstream behavioural or cognitive effects. Our framework weights source-target similarity by causal relevance while allowing for unknown causes, disabling differences and alternative routes to consciousness. Applied to biological systems, it explains why analogical support generally weakens with increasing causal distance from humans. Applied to contemporary AI, it suggests that behavioural similarity provides only limited evidence for consciousness because relevant causal correspondences remain poorly established. The framework also clarifies what evidence would strengthen claims of artificial consciousness.
Artificial Intelligence (cs.AI)
arXiv:2610.01002 [cs.AI]
(or arXiv:2610.01002v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.01002 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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