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ORCIDHilde J. P. Weerts
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2020 – today
- 2024
[j2]Hilde J. P. Weerts
, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. J. Artif. Intell. Res. 79: 639-677 (2024)- [c10]Hilde J. P. Weerts, Aislinn Kelly-Lyth, Reuben Binns, Jeremias Adams-Prassl:
Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms. FAccT 2024: 1850-1860 - [c9]Hilde J. P. Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy:
The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action. FAccT 2024: 2060-2070 - [c8]Adam Dubowski, Hilde J. P. Weerts, Anouk Wolters, Mykola Pechenizkiy:
Subgroup Harm Assessor: Identifying Potential Fairness-Related Harms and Predictive Bias. ECML/PKDD (8) 2024: 413-417 - [i10]Hilde J. P. Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy:
The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action. CoRR abs/2404.12143 (2024) - [i9]Hilde J. P. Weerts, Aislinn Kelly-Lyth, Reuben Binns, Jeremias Adams-Prassl:
Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms. CoRR abs/2404.14050 (2024) - 2023
- [j1]Hilde J. P. Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio:
Fairlearn: Assessing and Improving Fairness of AI Systems. J. Mach. Learn. Res. 24: 257:1-257:8 (2023) - [c7]Sheetal Borar, Hilde J. P. Weerts, Binyam Gebre, Mykola Pechenizkiy:
Improving Recommender System Diversity with Variational Autoencoders. BIAS 2023: 85-99 - [c6]Hilde J. P. Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy:
Algorithmic Unfairness Through the Lens of EU Non-Discrimination Law. EWAF 2023 - [c5]Hilde J. P. Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy:
Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree. FAccT 2023: 805-816 - [c4]Hilde J. P. Weerts, Renée Theunissen, Martijn C. Willemsen:
Look and You Will Find It: Fairness-Aware Data Collection through Active Learning. IAL@PKDD/ECML 2023: 74-88 - [i8]Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR abs/2303.08485 (2023) - [i7]Hilde J. P. Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio:
Fairlearn: Assessing and Improving Fairness of AI Systems. CoRR abs/2303.16626 (2023) - [i6]Hilde J. P. Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy:
Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree. CoRR abs/2305.13938 (2023) - 2022
- [c3]Dennis Collaris, Hilde J. P. Weerts, Daphne Miedema, Jarke J. van Wijk, Mykola Pechenizkiy:
Characterizing Data Scientists' Mental Models of Local Feature Importance. NordiCHI 2022: 9:1-9:12 - [i5]Hilde J. P. Weerts, Lambèr Royakkers, Mykola Pechenizkiy:
Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning. CoRR abs/2202.08536 (2022) - 2021
- [c2]Hilde Jacoba Petronella Weerts, Mykola Pechenizkiy:
Teaching Responsible Machine Learning to Engineers. Teaching ML 2021: 40-45 - [i4]Hilde J. P. Weerts:
An Introduction to Algorithmic Fairness. CoRR abs/2105.05595 (2021) - 2020
- [i3]Hilde J. P. Weerts, Andreas C. Müller, Joaquin Vanschoren:
Importance of Tuning Hyperparameters of Machine Learning Algorithms. CoRR abs/2007.07588 (2020)
2010 – 2019
- 2019
- [i2]Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy:
A Human-Grounded Evaluation of SHAP for Alert Processing. CoRR abs/1907.03324 (2019) - [i1]Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy:
Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models. CoRR abs/1907.03334 (2019) - 2017
- [c1]Wouter Duivesteijn, Tara Farzami, Thijs Putman, Evertjan Peer, Hilde J. P. Weerts, Jasper N. Adegeest, Gerson Foks, Mykola Pechenizkiy:
Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining. ECML/PKDD (3) 2017: 114-126
Coauthor Index
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