From indicators to biology: the calibration problem in artificial consciousness
Florentin Koch
arXiv Preprint Archive March 29, 2026 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Keywords | Cs.ai Q-bio.nc |
| Key points | Argues that probabilistic consciousness attribution to current AI systems is premature and proposes redirecting effort toward biologically grounded engineering that reduces the gap with living systems. |
Abstract
Recent work on artificial consciousness shifts evaluation from behaviour to internal architecture, deriving indicators from theories of consciousness and updating credences accordingly. This is progress beyond naive Turing-style tests. But the indicator-based programme remains epistemically under-calibrated: consciousness science is theoretically fragmented, indicators lack independent validation, and no ground truth of artificial phenomenality exists. Under these conditions, probabilistic consciousness attribution to current AI systems is premature. A more defensible near-term strategy is to redirect effort toward biologically grounded engineering -- biohybrid, neuromorphic, and connectome-scale systems -- that reduces the gap with the only domain where consciousness is empirically anchored: living systems.