213 citations · 600 across the 9 of their papers we have counts for
10 papers
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem
Sofie Goethals, David Martens, Theodoros Evgeniou
Artificial Intelligence (AI) systems are increasingly used in high-stakes domains of our life, increasing the need to explain these decisions and to make sure that they are aligned…
Epidemic Models for COVID-19 during the First Wave from February to May 2020: a Methodological Review
Marie Garin, Myrto Limnios, Alice Nicolaï +12
We review epidemiological models for the propagation of the COVID-19 pandemic during the early months of the outbreak: from February to May 2020. The aim is to propose a methodolog…
Understanding Consumer Preferences for Explanations Generated by XAI Algorithms
Yanou Ramon, Tom Vermeire, Olivier Toubia +2
Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainab…
Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data
Yanou Ramon, David Martens, Theodoros Evgeniou +1
Machine learning models on behavioral and textual data can result in highly accurate prediction models, but are often very difficult to interpret. Rule-extraction techniques have b…
Counterfactual Explanation Algorithms for Behavioral and Textual Data
Yanou Ramon, David Martens, Foster Provost +1
We study the interpretability of predictive systems that use high-dimensonal behavioral and textual data. Examples include predicting product interest based on online browsing data…
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data
Jorge Samper-González, Ninon Burgos, Simona Bottani +13
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of AD. However, they are difficult to reproduce because…