Summary of Diconstruct: Causal Concept-based Explanations Through Black-box Distillation, by Ricardo Moreira et al.
DiConStruct: Causal Concept-based Explanations through Black-Box Distillation
by Ricardo Moreira, Jacopo Bono, Mário Cardoso, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro
First submitted to arxiv on: 16 Jan 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 This paper introduces DiConStruct, a novel explanation method that generates local, interpretable explanations in the form of structural causal models and concept attributions. Unlike existing methods, DiConStruct is both concept-based and causal, allowing for reasoning about the explanations. The approach distills any black-box machine learning model while producing explanations efficiently, without compromising the predictive task’s performance. DiConStruct can be used with various image and tabular datasets to create more interpretable AI-driven decision-making systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DiConStruct is a new way to explain how artificial intelligence (AI) makes decisions. Usually, we can’t understand why AI does what it does, but this method helps by breaking down the reasoning into simple ideas that humans can grasp. This is important because people need to know why AI is making certain choices. The method works by simplifying complex AI models while still keeping their original predictions accurate. It’s a big step towards making AI more trustworthy and useful in our daily lives. |
Keywords
* Artificial intelligence * Machine learning
