Towards Fully Interpretable Deep Neural Networks: Are We There Yet?

We are not yet at full interpretability: The paper's title poses a question ("Are We There Yet?") and concludes that despite progress, fully interpretable deep neural networks remain an unrealized goal, indicating significant work remains before AI systems can be completely transparent to users.

Two main approaches exist but have different tradeoffs: The paper identifies post-hoc interpretation methods (explaining decisions after the fact) versus inherently interpretable DNNs (designed to be transparent from the start), with the latter receiving less research attention despite potentially offering more genuine transparency for building user trust.

Convolutional Neural Networks are a primary focus: The review concentrates on CNNs as a case study, suggesting that even within a specific neural network architecture, achieving full interpretability presents substantial technical challenges that the field has not yet overcome.

Current methods address "different interpretation requirements" unevenly: The paper notes that existing interpretable DNN methods cater to varying needs, implying that no single solution works universally—organizations may need different interpretability approaches depending on their specific use case or regulatory context.

Significant research gaps remain: The authors identify gaps in current work and suggest future research directions are needed, indicating that the path toward trustworthy, fully interpretable AI systems requires continued innovation beyond existing techniques.