Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks

Understanding AI internals is crucial for safety and trust: As deep neural networks become more powerful and are deployed in real-world applications, we cannot rely on them without rigorous understanding of how they work. The survey emphasizes that interpretability tools are essential for identifying problems, fixing bugs, and building more trustworthy AI systems.

Multiple approaches exist to peek inside AI "black boxes": The survey categorizes over 300 interpretability methods by what they explain (weights, individual neurons, subnetworks, or learned representations) and when they're applied (during training or after). This taxonomy helps practitioners choose appropriate tools depending on what aspect of the network they need to understand.

Interpretability connects to broader AI safety concerns: The research reveals important links between interpretability and other critical areas including adversarial robustness (how AI systems handle attacks), continual learning (how systems learn over time without forgetting), and network compression (making AI more efficient). Understanding these connections helps address multiple responsible AI challenges simultaneously.

Current interpretability research faces significant gaps: The survey identifies that the field lacks standardization and systematic evaluation of methods, suggesting that many interpretability tools may not be as reliable or comparable as practitioners assume. This highlights the need for more rigorous validation before deploying these techniques in high-stakes applications.