Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …

In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …

Feature attributions are a common paradigm for model explanations due to their simplicity in
assigning a single numeric score for each input feature to a model. In the actionable …

Adversarial robustness assessment for video recognition models has raised concerns owing
to their wide applications on safety-critical tasks. Compared with images, videos have much …

Explainability and Safety engender trust. These require a model to exhibit consistency and
reliability. To achieve these, it is necessary to use and analyze data and knowledge with …

Meta-heuristic algorithms have been extensively utilized in feature selection tasks because
they can obtain the global optimal solution. However, the meta-heuristic algorithm will take …

Abstract Machine learning algorithms are widely applied to create powerful prediction
models. With increasingly complex models, humans' ability to understand the decision …

Uplift modeling has shown very promising results in online marketing. However, most
existing works are prone to the robustness challenge in some practical applications. In this …

The problem of algorithmic recourse has been explored for supervised machine learning
models, to provide more interpretable, transparent and robust outcomes from decision …

The Impact of Using Constraints on Counterfactual Explanations Page 1 The Impact of Using
Constraints on Counterfactual Explanations Maciej Falbogowski, Jerzy Stefanowski, Zuzanna …