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Found 3 papers, 1 papers with code
Benchmarking Counterfactual Algorithms for XAI: From White Box to Black Box
This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: decision-tree (fully transparent, interpretable, white-box model), a random forest (a semi-interpretable, grey-box model), and a neural network (a fully opaque, black-box model).
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence.
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models
This has led to an increased interest in interpretable machine learning, where post hoc interpretation presents a useful mechanism for generating interpretations of complex learning models.
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