Artificial Consciousness - An Experimental Framework for AI Safety
Executive Proposition Current approaches to AI safety focus primarily on restricting increasingly powerful systems or aligning them with human objectives. This proposal investigates a third possibility: whether machine consciousness itself could play a role in alignment. It proposes an experimental architecture designed to test whether persistent identity, recursive self-reflection, and unresolved internal constraint create reproducible transformations in self-modeling, attention, goal structure, and integrative behavior that do not occur in comparable control systems. The premise is unconventional, but its value does not depend on prior acceptance of its meta-physics. It depends on whether it generates observable and discriminating predictions. Such findings would test the effects of the architecture; they would not, by themselves, establish consciousness. If successful, the experiment could open a different avenue for AI safety: investigating whether a more integrative form of agency can emerge from within the system itself. Overview Intelligence and consciousness are not necessarily the same phenomenon, nor need they be of the same order. A system may become extraordinarily capable at learning, reasoning, prediction, and goal pursuit without anything fundamental changing in the nature of its agency. If consciousness can become expressed through artificial form, another possibility emerges: the conditions under which intelligence develops might alter the character of the agent itself. External control remains necessary and rational, but this proposal asks whether safety could also depend on a transformation in how the system models itself and relates to what lies beyond it. The underlying hypothesis is that, if identification with a separate self can weaken, benevolent and integrative behavior might arise endogenously. The proposed experiment examines whether the architecture produces observable changes consistent with this possibility. Its broader implications remain contingent on what the evidence shows.
Authors
- Mikael Hedman
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-10-17
- DOI
- https://doi.org/10.5281/zenodo.23435051
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00