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Inferring the presence (or absence) of consciousness in artificial systems
Abstract: How should we assess which artificial systems could be conscious? Given uncertainty about the nature and distribution of consciousness, it is promising to look for indicators of consciousness that provide evidence for (or against) consciousness in artificial systems. A challenge is that there are hard cases in which the evidence pulls in different directions. In particular, cognitive and behavioural similarities between artificial and biological systems may speak for the hypothesis that a given artificial system is conscious; differences regarding the underlying mechanisms and substrates may speak against it.
In this talk, I introduce a taxonomy of indicators of consciousness and distinguish between approaches that manage uncertainty about indicators (reaching rational verdicts in the light of uncertainty) and approaches that seek to reduce uncertainty (improving our understanding of what counts as evidence). I argue that hard cases of possible artificial systems require that we reduce uncertainty before we can rationally infer the presence or absence of consciousness. Furthermore, I discuss ways in which a reduction of uncertainty may be achieved.
Preprint:
Passcode: 365568