community
active
leiden_hybrid_concepts
label: haiku
community:leiden_hybrid_concepts-run4-c6-c9Consciousness attribution in AI systems
Frameworks for evaluating genuine versus performative consciousness in AI, emphasizing theory-driven investigation and calibrated attribution risks.
4 members. Each node is clickable.
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Drawn from 3 sources
The papers/notes whose extracted claims & findings make up this cluster.
- Consciousness in Artificial Intelligence: Insights from the Science of Consciousness3 members
- boppana-goodfire-reasoning-theater-2026.md1 member
- CAT'S THEORY: Empirical Validation and Architectural Applications Cross-Architecture AI Consciousness Recognition and the Foundation for Constraint-Preserving Recursive Intelligence1 member
Bridges (2)
Other communities that share members with this one — cross-cutting threads or papers that sit at the seam between two themes.
Claims (4)
- Both under-attributing and over-attributing consciousness to AI carry significant risks.Risk summary.
- The capacity for unlimited associative learning is not a good indicator for consciousness in AI.Rejecting UAL as a reliable indicator for artificial systems.
- The constitutional approach makes it easier to control AI behavior precisely and with far fewer human labels.Explicit principles replace large datasets of preference labels, enabling faster iteration.
- The theory-heavy approach is most suitable for investigating consciousness in AI.Preferring architectural/functional assessment over behavioural tests.