Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining. - Wouter Duivesteijn; Tara Farzami; Thijs Putman; Evertjan Peer; Hilde J. P. Weerts; Jasper N. Adegeest; Gerson Foks; Mykola Pechenizkiy (ECML/PKDD (3), 2017)

A Human-Grounded Evaluation of SHAP for Alert Processing. - Hilde J. P. Weerts; Werner van Ipenburg; Mykola Pechenizkiy (CoRR, 2019)

Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models. - Hilde J. P. Weerts; Werner van Ipenburg; Mykola Pechenizkiy (CoRR, 2019)

Importance of Tuning Hyperparameters of Machine Learning Algorithms. - Hilde J. P. Weerts; Andreas C. Müller 002; Joaquin Vanschoren (CoRR, 2020)

Teaching Responsible Machine Learning to Engineers. - Hilde Jacoba Petronella Weerts; Mykola Pechenizkiy (Teaching ML, 2021)

An Introduction to Algorithmic Fairness. - Hilde J. P. Weerts (CoRR, 2021)

Characterizing Data Scientists' Mental Models of Local Feature Importance. - Dennis Collaris; Hilde J. P. Weerts; Daphne Miedema; Jarke J. van Wijk; Mykola Pechenizkiy (NordiCHI, 2022)

Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning. - Hilde J. P. Weerts; Lambèr Royakkers; Mykola Pechenizkiy (CoRR, 2022)

Improving Recommender System Diversity with Variational Autoencoders. - Sheetal Borar; Hilde J. P. Weerts; Binyam Gebre; Mykola Pechenizkiy (BIAS, 2023)

Algorithmic Unfairness Through the Lens of EU Non-Discrimination Law. - Hilde J. P. Weerts; Raphaële Xenidis; Fabien Tarissan; Henrik Palmer Olsen; Mykola Pechenizkiy (EWAF, 2023)

Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree. - Hilde J. P. Weerts; Raphaële Xenidis; Fabien Tarissan; Henrik Palmer Olsen; Mykola Pechenizkiy (FAccT, 2023)

Look and You Will Find It: Fairness-Aware Data Collection through Active Learning. - Hilde J. P. Weerts; Renée Theunissen; Martijn C. Willemsen (IAL@PKDD/ECML, 2023)

Fairlearn: Assessing and Improving Fairness of AI Systems. - Hilde J. P. Weerts; Miroslav Dudík; Richard Edgar; Adrin Jalali; Roman Lutz; Michael Madaio (J. Mach. Learn. Res., 2023)

Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms. - Hilde J. P. Weerts; Aislinn Kelly-Lyth; Reuben Binns; Jeremias Adams-Prassl (FAccT, 2024)

The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action. - Hilde J. P. Weerts; Raphaële Xenidis; Fabien Tarissan; Henrik Palmer Olsen; Mykola Pechenizkiy (FAccT, 2024)

Subgroup Harm Assessor: Identifying Potential Fairness-Related Harms and Predictive Bias. - Adam Dubowski; Hilde J. P. Weerts; Anouk Wolters; Mykola Pechenizkiy (ECML/PKDD (8), 2024)

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. - Hilde J. P. Weerts; Florian Pfisterer; Matthias Feurer; Katharina Eggensperger; Edward Bergman; Noor H. Awad; Joaquin Vanschoren; Mykola Pechenizkiy; Bernd Bischl; Frank Hutter (J. Artif. Intell. Res., 2024)

Fourth European Workshop on Algorithmic Fairness (EWAF'25). - Hilde J. P. Weerts; Mykola Pechenizkiy; Doris Allhutter; Ana Maria Corrêa; Thomas Grote; Cynthia Liem (EWAF, 2025)