On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
Authors: Eoin M. Kenny, Mark T Keane11575-11585
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Two controlled experiments compare this method to others in the literature, showing that PIECE generates highly plausible counterfactuals (and the best semi-factuals) on several benchmark measures. |
| Researcher Affiliation | Academia | Eoin M. Kenny*, Mark T. Keane University College Dublin, Dublin, Ireland Insight Centre for Data Analytics, UCD, Dublin, Ireland Vista Milk SFI Research Centre eoin.kenny@insight-centre.org, mark.keane@ucd.ie |
| Pseudocode | Yes | Algorithm 1: Modify exceptional features in x to produce x |
| Open Source Code | Yes | The code needed to reproduce our algorithm may be found at https://github.com/Eoin Kenny/AAAI-2021. |
| Open Datasets | Yes | Fig. 3 illustrates some of PIECE s plausible contrastive explanations for a CNN s classifications on the MNIST dataset (Le Cun, Cortes, and Burges 2010) |
| Dataset Splits | No | For MNIST, a test-set of 163 images classified by the CNN was used which divided into: (i) correct classifications (N=60) with six examples per number-class, (ii) close-correct classifications (N=62), that had an output Soft Max probability < 0.8, where the CNN just got the classification right,3 and (iii) incorrect classifications (N=41) by the CNN (i.e., every instance misclassified by the CNN). For CIFAR-10, the test-set was divided into: (i) correct classifications (N=30) with three examples per class, and (ii) incorrect classifications (N=30) with three examples per class. All instances were randomly selected, with the exception of MNIST s incorrect classifications, which were not randomly selected as there was only 41 of them. |
| Hardware Specification | No | The paper mentions 'utilizing a GPU' as a general possibility to reduce optimization time but does not provide specific hardware details (e.g., GPU models, CPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | No | Hyperparameter choices are presented in Section S2 of the supplementary material. |