Summary of Neural Networks Decoded: Targeted and Robust Analysis Of Neural Network Decisions Via Causal Explanations and Reasoning, by Alec F. Diallo et al.
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
by Alec F. Diallo, Vaishak Belle, Paul Patras
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces TRACER, a novel method that estimates the causal dynamics underlying deep neural network (DNN) decisions without altering their architecture or compromising performance. The approach intervenes on input features to observe how changes propagate through the network, allowing for the determination of feature importance and construction of a high-level causal map. This provides a structured and interpretable view of how different parts of the network influence decisions. TRACER also generates counterfactuals that reveal possible model biases and offer contrastive explanations for misclassifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TRACER is a new way to understand how deep learning models work. It helps us figure out why they make certain decisions by looking at what happens when we change the input information. This lets us see which parts of the model are most important, and it can even show us where the model might be biased or making mistakes. By using this method, we can get a better understanding of how deep learning models work and make them more useful. |
Keywords
» Artificial intelligence » Deep learning » Neural network
