ExplainabilityTransparencyTrustworthinesscs.LG
SpecXAI -- Spectral interpretability of Deep Learning Models
Stefan Druc, Peter Wooldridge, Adarsh Krishnamurthy, Soumik Sarkar, Aditya Balu
February 20, 2023
arXiv.org
1 citations
Key Takeaways
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Deep learning models can be made more interpretable through spectral analysis: SpecXAI offers a new technical approach to understanding how neural networks make decisions by analyzing their spectral characteristics, addressing the "black box" problem that currently limits adoption of AI in business and industry.
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Interpretability and performance don't have to be mutually exclusive: The framework demonstrates that it's possible to transform complex deep learning models into simpler, linear symbolic representations while maintaining their decision-making capabilities—suggesting that explainability doesn't require sacrificing model effectiveness.
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Understanding network behavior is crucial for responsible deployment: The paper emphasizes that despite strong performance metrics, the inability to understand how deep learning models work "could prevent their use" in real-world applications, highlighting why interpretability methods are essential for responsible AI adoption.
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New tools are emerging to demystify AI decision-making: SpecXAI represents progress in the broader field of Explainable AI (XAI), which aims to develop techniques that make neural networks' behavior more transparent and understandable to stakeholders who need to trust and validate these systems.
Abstract
Deep learning is becoming increasingly adopted in business and industry due to its ability to transform large quantities of data into high-performing models. These models, however, are generally regarded as black boxes, which, in spite of their performance, could prevent their use. In this context, the field of eXplainable AI attempts to develop techniques that temper the impenetrable nature of the models and promote a level of understanding of their behavior. Here we present our contribution to XAI methods in the form of a framework that we term SpecXAI, which is based on the spectral characterization of the entire network. We show how this framework can be used to not only understand the network but also manipulate it into a linear interpretable symbolic representation.