arXiv:2601.12913v4 Announce Type: replace Abstract: This paper argues that interpretability research in Artificial Intelligence (AI) is fundamentally ill-posed as existing definitions of interpretability fail to describe how interpretability can be formally tested or designed for. We posit that actionable definitions of interpretability must be formulated in terms of *symmetries* that inform model design and lead to testable conditions. Under a probabilistic view, we hypothesise that four symmetries (inference equivariance, information invariance, concept-closure invariance, and structural inv
The proliferation of complex AI models necessitates a more rigorous framework for interpretability, moving beyond ad-hoc methods to formal, testable definitions.
A formal, symmetry-based approach to AI interpretability could unlock greater trust, reliability, and deployment of advanced AI systems in critical applications.
The focus of AI interpretability research shifts from descriptive explanations to prescriptive design principles, influencing how future AI models are built and evaluated.
- · AI safety researchers
- · Developers of mission-critical AI
- · Regulatory bodies
- · Ethical AI frameworks
- · Ad-hoc interpretability tooling vendors
- · Black-box AI proponents
Increased research and development into formal interpretability methods based on symmetries.
New AI architectures designed from the ground up with embedded interpretability features, leading to more robust and explainable models.
Accelerated adoption of AI in highly regulated industries due to demonstrable and testable interpretability leading to broader societal integration.
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Read at arXiv cs.AI