Efficient computation of counterfactual explanations of LVQ models
arXiv:1908.00735
Abstract
The increasing use of machine learning in practice and legal regulations like EU's GDPR cause the necessity to be able to explain the prediction and behavior of machine learning models. A prominent example of particularly intuitive explanations of AI models in the context of decision making are counterfactual explanations. Yet, it is still an open research problem how to efficiently compute counterfactual explanations for many models. We investigate how to efficiently compute counterfactual explanations for an important class of models, prototype-based classifiers such as learning vector quantization models. In particular, we derive specific convex and non-convex programs depending on the used metric.
arXiv commentShort version accepted at ESANN-2020
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Cited by in corpus (4)
- A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
- A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
- On the computation of counterfactual explanations -- A survey
- Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test