arXiv:2606.19850v1 Announce Type: new Abstract: Deep neural networks (DNNs) exhibit attractive performance in various fields but often suffer from low interpretability. The neural additive model (NAM) and its variant called the neural basis model (NBM) use neural networks (NNs) as nonlinear shape functions in generalized additive models (GAMs). Both models are highly interpretable and exhibit good performance and flexibility for NN training. NAM and NBM can provide and visualize the contribution of each feature to the prediction owing to GAM-based architectures. However, when using two-input N
The continuous drive for more transparent and interpretable AI models, especially as deep learning adoption grows, makes research in this area timely.
Improved interpretability in deep neural networks addresses a key adoption barrier in sensitive applications by making AI decisions more understandable and trustworthy.
This research contributes to making complex deep learning models more amenable to analysis, potentially broadening their application in fields requiring high transparency.
- · AI ethicists
- · Healthcare sector
- · Financial services
- · Researchers in explainable AI
- · Black-box AI model developers (relatively)
Increased capability to explain the outputs of complex AI models.
Reduced regulatory hurdles for AI deployment in critical sectors due to enhanced transparency.
Broader public trust and faster integration of AI into regulated industries, shifting focus from 'if' AI should be used to 'how' it is best deployed.
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Read at arXiv cs.LG