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Found 2 papers, 1 papers with code
Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization
To this end, we present a novel framework for the generation of counterfactual examples which formulates its goal as a multi-objective optimization problem balancing three different objectives: 1) plausibility, i. e., the likeliness of the counterfactual of being possible as per the distribution of the input data; 2) intensity of the changes to the original input; and 3) adversarial power, namely, the variability of the model's output induced by the counterfactual.
Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples
The last decade has witnessed the proliferation of Deep Learning models in many applications, achieving unrivaled levels of predictive performance.
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