David Martens

About David Martens

David Martens, With an exceptional h-index of 43 and a recent h-index of 35 (since 2020), a distinguished researcher at Universiteit Antwerpen, specializes in the field of Data mining, Explainable AI, Mining Behavioral Data, Data Science Ethics, Responsible AI.

His recent articles reflect a diverse array of research interests and contributions to the field:

Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics

Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem

Calculating and Visualizing Counterfactual Feature Importance Values

Unveiling the Potential of Counterfactuals Explanations in Employability

Disagreement amongst counterfactual explanations: How transparency can be deceptive

Nice: an algorithm for nearest instance counterfactual explanations

How Can IJDS Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Monetizing explainable ai: A double-edged sword

David Martens Information

University

Position

___

Citations(all)

8200

Citations(since 2020)

4106

Cited By

5681

hIndex(all)

43

hIndex(since 2020)

35

i10Index(all)

85

i10Index(since 2020)

65

Email

University Profile Page

Google Scholar

David Martens Skills & Research Interests

Data mining

Explainable AI

Mining Behavioral Data

Data Science Ethics

Responsible AI

Top articles of David Martens

Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics

arXiv preprint arXiv:2401.13391

2024/1/24

Toon Calders
Toon Calders

H-Index: 25

David Martens
David Martens

H-Index: 31

Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem

arXiv preprint arXiv:2306.13885

2023/6/24

David Martens
David Martens

H-Index: 31

Calculating and Visualizing Counterfactual Feature Importance Values

arXiv preprint arXiv:2306.06506

2023/6/10

David Martens
David Martens

H-Index: 31

Unveiling the Potential of Counterfactuals Explanations in Employability

arXiv preprint arXiv:2305.10069

2023/5/17

David Martens
David Martens

H-Index: 31

Disagreement amongst counterfactual explanations: How transparency can be deceptive

2023/4/25

David Martens
David Martens

H-Index: 31

Nice: an algorithm for nearest instance counterfactual explanations

Data mining and knowledge discovery

2023/4/7

Pieter Leyman
Pieter Leyman

H-Index: 5

David Martens
David Martens

H-Index: 31

How Can IJDS Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

INFORMS Journal on Data Science

2023/4

Monetizing explainable ai: A double-edged sword

arXiv preprint arXiv:2304.06483

2023/3/27

Shapley value for tax audit data valuation

2023

David Martens
David Martens

H-Index: 31

Ann Jorissen
Ann Jorissen

H-Index: 16

Explainability methods to detect and measure discrimination in machine learning models

2023

David Martens
David Martens

H-Index: 31

Toon Calders
Toon Calders

H-Index: 25

The Impact of Cloaking Digital Footprints on User Privacy and Personalization

arXiv preprint arXiv:2312.15000

2023/12/22

Foster Provost
Foster Provost

H-Index: 42

David Martens
David Martens

H-Index: 31

Tell Me a Story! Narrative-Driven XAI with Large Language Models

arXiv preprint arXiv:2309.17057

2023/9/29

David Martens
David Martens

H-Index: 31

Explainable AI for operational research: A defining framework, methods, applications, and a research agenda

2023/9/22

A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data

European Journal of Operational Research

2023/8/24

Kenneth Sörensen
Kenneth Sörensen

H-Index: 33

David Martens
David Martens

H-Index: 31

The privacy issue of counterfactual explanations: explanation linkage attacks

ACM Transactions on Intelligent Systems and Technology

2023/8/12

Kenneth Sörensen
Kenneth Sörensen

H-Index: 33

David Martens
David Martens

H-Index: 31

The non-linear nature of the cost of comprehensibility

Journal of Big Data

2022/12

David Martens
David Martens

H-Index: 31

How sustainable is" common" data science in terms of power consumption?

arXiv preprint arXiv:2207.01934

2022/7/5

David Martens
David Martens

H-Index: 31

Explainable image classification with evidence counterfactual

2022/5

David Martens
David Martens

H-Index: 31

Barriers to academic data science research in the new realm of algorithmic behaviour modification by digital platforms

2022/4

Data science ethics: concepts, techniques, and cautionary tales

2022/3/24

David Martens
David Martens

H-Index: 31

See List of Professors in David Martens University(Universiteit Antwerpen)

Co-Authors

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