arXiv:2606.24716v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy metrics or qualitative inspection rather than measuring semantic correspondence. We present a human-grounded evaluation framework that quantifies alignment between SAE latents and human-annotated concepts, without requiring user studies, and validate this matching through targeted attribute perturbations. To enable this intervention-style evaluation in vision, we constru
The increasing adoption and complexity of sparse autoencoders across AI research necessitates more robust and reliable interpretability frameworks to understand their internal representations.
Improved interpretability of AI models is crucial for ensuring their reliability, safety, and trustworthiness, particularly in high-stakes applications, fostering greater adoption and reducing regulatory friction.
The ability to quantify alignment between SAE latents and human-annotated concepts without user studies provides a more scalable and empirical method for evaluating AI interpretability.
- · AI researchers
- · Developers of interpretable AI
- · Industries deploying AI in sensitive applications
- · Black-box AI development approaches
- · Systems relying solely on proxy metrics for interpretability
More rigorous and scalable evaluation of sparse autoencoder interpretability becomes possible.
This improved understanding could accelerate the development of more transparent and controllable AI systems, particularly in computer vision and multimodal models.
Increased trust and reduced regulatory hurdles for AI deployment could lead to broader commercialization of advanced AI technologies.
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Read at arXiv cs.AI