Summary of A Framework For Feasible Counterfactual Exploration Incorporating Causality, Sparsity and Density, by Kleopatra Markou et al.
A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density
by Kleopatra Markou, Dimitrios Tomaras, Vana Kalogeraki, Dimitrios Gunopulos
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper explores the generation of counterfactual (CF) explanations for machine learning models, which involve small perturbations to input data. The authors examine whether feasible and useful CF examples can be produced using benchmark datasets. They employ a black box model as a classifier and a Variational Autoencoder (VAE) to generate these examples. The results show that the proposed approach successfully generates sparse and feasible CF examples that satisfy predefined causal constraints, as confirmed by attributes in each dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is trying to figure out how to make machines understand why they made certain decisions. They want to know if it’s possible to change a little bit of what went into a machine learning model and still get useful answers. The authors use special computer programs to try this out with different sets of data. They find that they can do this successfully, which could be very helpful in real-life situations. |
Keywords
» Artificial intelligence » Machine learning » Variational autoencoder
