From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?
arXiv:2512.15134v2 Announce Type: replace-cross Abstract: A goal of interpretability is to recover disentangled representations of latent concepts (features) from the activations of neural networks. The quality of features is typically evaluated in isolation, and under implicit independence assumptions that may not hold in practice. Thus, it is unclear to what extent common featurization methods such as sparse autoencoders (SAEs) and probes disentangle one concept from another. We propose a multi-concept evaluation setting using concepts including sentiment, domain, voice, and tense. We evalua
The proliferation of advanced neural networks makes understanding and controlling their internal representations crucial for safety, reliability, and further development.
Improving interpretability methods for AI models is essential for developing trustworthy AI and enabling more robust, controllable, and explainable autonomous systems.
The proposed multi-concept evaluation setting offers a more rigorous framework for assessing how effectively interpretability methods disentangle latent concepts within AI models.
- · AI safety researchers
- · AI developers
- · Organizations deploying critical AI systems
- · Black-box AI systems
- · Unreliable interpretability methods
Researchers gain new tools to evaluate and improve the disentanglement capabilities of interpretability methods like sparse autoencoders and probes.
More interpretable AI models could accelerate development in areas requiring high trust and transparency, such as medical AI or autonomous vehicles.
Enhanced interpretability may lead to new architectural insights for neural networks, fostering the creation of inherently more transparent and controllable AI systems.
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