Interpretive Validation

CIMC's methodology for evaluating whether a built system is conscious: combining multiple forms of evidence including predicted functional organization and developmental trajectories

Neighborhood — ranked by edge-count

Papers (1)

paper

cimcWhitepaper
introduces

Methods (1)

method

Interpretive Analysis of Internal Structure
implements
CIMC's proposed evaluation methodology: examining what systems build within themselves and inferring to best explanation

Questions (1)

question

what would constitute adequate evidence for consciousness in artificial systems?
answered_by
Methodological question driving CIMC's development of interpretive validation over behavioral testing

Hypotheses (1)

hypothesis

General computational machines with sufficient resources possess the necessary and sufficient means to implement consciousness
associated_with
CIMC's central testable hypothesis grounding the entire research program

Concepts (1)

concept

Representational Embedding Spaces
supports
Internal structure of AI systems that CIMC proposes to analyze interpretively to evaluate consciousness hypotheses

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

interpretabilityconcept0.842
The capability to explain model predictions; a central theme of the paper, with disruption profiles as vehicle.
interpretative methodmethod0.815
The historical/hermeneutic approach adopted by the paper to analyze cybernetic diagrams in light of Flusser’s philosophy.
Automated Interpretabilityframework0.778
Method using large language models (Claude) to generate and test explanations of features at scale
Interpretability as Natural Scienceframework0.771
Proposed paradigm for evaluating interpretability work through empirical falsifiability rather than benchmarks or user studies
interpretive abstraction (method)method0.771
Programming technique to restructure a fine-grained Linda program for efficiency by replacing live data structures with passive ones and coarser-grain processes.
Interpretability Illusionconcept0.771
Cases where subspace interventions change model behaviour through parallel pathways rather than the target feature
"For interpretability, I don't think we even have the right definitions."quote0.753
Ian Goodfellow quote used to illustrate the pre-paradigmatic state of interpretability research
Interpretability-Driven Feedback Steeringconcept0.751
Framework of using internal-state representations to control or steer generative models; conceptually parallel to manifold steering in language models.