22 citations · 63 across the 5 of their papers we have counts for
8 papers
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
Carlos Mougan, Klaus Broelemann, Gjergji Kasneci +2
As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In the past, predictive performance was considered the key indicator…
On Counterfactual Explanations under Predictive Multiplicity
Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
Counterfactual explanations are usually obtained by identifying the smallest change made to an input to change a prediction made by a fixed model (hereafter called sparse methods).…
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Johannes Haug, Martin Pawelczyk, Klaus Broelemann +1
Feature selection can be a crucial factor in obtaining robust and accurate predictions. Online feature selection models, however, operate under considerable restrictions; they need…
Bias in Data-driven AI Systems -- An Introductory Survey
Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju +20
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and any…
Learning Model-Agnostic Counterfactual Explanations for Tabular Data
Martin Pawelczyk, Johannes Haug, Klaus Broelemann +1
Counterfactual explanations can be obtained by identifying the smallest change made to a feature vector to qualitatively influence a prediction; for example, from 'loan rejected' t…
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees
Klaus Broelemann, Gjergji Kasneci
Machine learning algorithms aim at minimizing the number of false decisions and increasing the accuracy of predictions. However, the high predictive power of advanced algorithms co…