Calibration and transfer in indicator-based assessments of artificial consciousness.

Abstract

Research on artificial consciousness increasingly shifts evaluation from behaviour to internal architecture. Theory-based indicators are used to update probability assignments. This improves on behavioural tests but raises two distinct problems. First, these assignments cannot currently be calibrated against independently established artificial consciousness outcomes. Second, their evidential relevance is transferred from biological cases without independent support that indicator-consciousness relations remain stable across substrates. This commentary distinguishes calibration from transfer and adapts the iterative natural-kind strategy by proposing a preliminary, theory-relative comparative space for cross-substrate assessment.