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08/22/2023
Interpretable Distribution-Invariant Fairness Measures for Continuous Scores
Measures of algorithmic fairness are usually discussed in the context of...
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07/25/2023
Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Space
Counterfactual Explanations (CEs) are an important tool in Algorithmic R...
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03/14/2023
Explanation Shift: Investigating Interactions between Models and Shifting Data Distributions
As input data distributions evolve, the predictive performance of machin...
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10/22/2022
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
As input data distributions evolve, the predictive performance of machin...
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03/30/2022
Dynamic Model Tree for Interpretable Data Stream Learning
Data streams are ubiquitous in modern business and society. In practice,...
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06/23/2020
On Counterfactual Explanations under Predictive Multiplicity
Counterfactual explanations are usually obtained by identifying the smal...
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06/18/2020
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Feature selection can be a crucial factor in obtaining robust and accura...
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01/14/2020
Bias in Data-driven AI Systems – An Introductory Survey
AI-based systems are widely employed nowadays to make decisions that hav...
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10/21/2019
Towards User Empowerment
Counterfactual explanations can be obtained by identifying the smallest ...
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09/25/2018
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees
Machine learning algorithms aim at minimizing the number of false decisi...
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07/27/2018
Combining Restricted Boltzmann Machines with Neural Networks for Latent Truth Discovery
Latent truth discovery, LTD for short, refers to the problem of aggregat...
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12/31/2017