75 citations · 252 across the 11 of their papers we have counts for
11 papers
Monetizing Explainable AI: A Double-edged Sword
Travis Greene, Sofie Goethals, David Martens +1
Algorithms used by organizations increasingly wield power in society as they decide the allocation of key resources and basic goods. In order to promote fairer, juster, and more tr…
The privacy issue of counterfactual explanations: explanation linkage attacks
Sofie Goethals, Kenneth Sörensen, David Martens
Black-box machine learning models are being used in more and more high-stakes domains, which creates a growing need for Explainable AI (XAI). Unfortunately, the use of XAI in machi…
Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data
Yanou Ramon, Sandra C. Matz, R. A. Farrokhnia +1
Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints i…
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
Raphael Mazzine, David Martens
Counterfactual explanations are viewed as an effective way to explain machine learning predictions. This interest is reflected by a relatively young literature with already dozens…
How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice
Tom Vermeire, Thibault Laugel, Xavier Renard +2
Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Vari…
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…
NICE: An Algorithm for Nearest Instance Counterfactual Explanations
Dieter Brughmans, Pieter Leyman, David Martens
In this paper we suggest NICE: a new algorithm to generate counterfactual explanations for heterogeneous tabular data. The design of our algorithm specifically takes into account a…
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…
Dance Hit Song Prediction
Dorien herremans, David Martens, Kenneth Sörensen
Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the…
Explaining Classification Models Built on High-Dimensional Sparse Data
Julie Moeyersoms, Brian d'Alessandro, Foster Provost +1
Predictive modeling applications increasingly use data representing people's behavior, opinions, and interactions. Fine-grained behavior data often has different structure from tra…