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