Summary of Unified Explanations in Machine Learning Models: a Perturbation Approach, by Jacob Dineen et al.
Unified Explanations in Machine Learning Models: A Perturbation Approach
by Jacob Dineen, Don Kridel, Daniel Dolk, David Castillo
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Machine learning educators can expect a paradigm shift towards Explainable Artificial Intelligence (XAI) in recent years. Complex ML models have excelled in various tasks, and now the focus is shifting from traditional metrics to understanding what these models are telling us about our data and how they arrive at conclusions. The inconsistency between XAI and modeling techniques raises doubts on the effectiveness of explainability approaches. To address this issue, a systematic perturbation-based analysis is proposed against SHapley Additive exPlanations (Shap), a popular model-agnostic method in XAI. This includes generating relative feature importance in dynamic inference settings using various ML and deep learning methods, along with metrics to quantify the performance of explanations generated under static conditions. Furthermore, a taxonomy for feature importance methodology is proposed, examining alignment, and observing quantifiable similarity amongst explanation models across several datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Explainable Artificial Intelligence (XAI) is helping us understand how machine learning models work. Right now, we have complex models that are good at doing things, but we’re starting to wonder what they’re really telling us about our data. Some methods for explaining these models don’t match up well with how the models were trained. To fix this problem, researchers are proposing a new way to analyze one popular method called SHapley Additive exPlanations (Shap). They want to see how well explanations work when the model is changing or adapting. |
Keywords
* Artificial intelligence * Alignment * Deep learning * Inference * Machine learning
