From indicators to biology: the calibration problem in artificial consciousness

📰 ArXiv cs.AI

The calibration problem in artificial consciousness arises from the lack of independent validation of indicators and the absence of a ground truth for artificial phenomenality

advanced Published 31 Mar 2026

Action Steps

Recognize the limitations of indicator-based evaluation methods for artificial consciousness
Understand the theoretical fragmentation of consciousness science and its impact on indicator validation
Develop new methods for independent validation of indicators and establishing a ground truth for artificial phenomenality
Integrate insights from biology and neuroscience to improve the calibration of artificial consciousness evaluation methods

Who Needs to Know This

AI researchers and cognitive scientists benefit from understanding the calibration problem in artificial consciousness to develop more accurate evaluation methods, while software engineers and AI engineers can apply this knowledge to improve the design of artificial conscious systems

Key Insight

💡 The calibration problem in artificial consciousness requires addressing the lack of independent validation of indicators and the absence of a ground truth for artificial phenomenality