Summary of Explainable Anomaly Detection: Counterfactual Driven What-if Analysis, by Logan Cummins et al.
Explainable Anomaly Detection: Counterfactual driven What-If Analysis
by Logan Cummins, Alexander Sommers, Sudip Mittal, Shahram Rahimi, Maria Seale, Joseph Jaboure, Thomas Arnold
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 Predictive maintenance is a crucial field that involves three main areas: anomaly detection, fault diagnosis, and remaining useful life prediction. Anomaly detection identifies issues, but it doesn’t provide insight into the root cause or solution. Explainable AI offers counterfactual explanations that can suggest changes to reverse an issue. However, these suggestions might not be actionable, prompting questions about alternative solutions. This paper explores using counterfactual explanations as what-if analysis for predictive maintenance, applying a temporal convolutional network on the PRONOSTIA dataset. The proposed method presents counterfactuals in the form of what-if scenarios to inspire future work on more complex systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive maintenance is like predicting when your car might break down. There are three main areas: finding problems, diagnosing them, and guessing how much longer something will last. When we find a problem, it’s like saying “oh no, my car is broken!” But that doesn’t tell us why it broke or how to fix it. Explainable AI can help by giving suggestions on what changes to make to avoid the issue. However, these suggestions might not be helpful, making us wonder what would happen if we did something different. This research explores using this technology for predictive maintenance, applying it to a dataset of real-world problems. The goal is to inspire new ideas for more complex systems. |
Keywords
» Artificial intelligence » Anomaly detection » Convolutional network » Prompting
