75 citations · 252 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…