115 citations · 216 across the 9 of their papers we have counts for
9 papers
Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms
Hilde Weerts, Aislinn Kelly-Lyth, Reuben Binns +1
Emerging scholarship suggests that the EU legal concept of direct discrimination - where a person is given different treatment on grounds of a protected characteristic - may apply…
The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action
Hilde Weerts, Raphaële Xenidis, Fabien Tarissan +2
Various metrics and interventions have been developed to identify and mitigate unfair outputs of machine learning systems. While individuals and organizations have an obligation to…
Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree
Hilde Weerts, Raphaële Xenidis, Fabien Tarissan +2
Concerns regarding unfairness and discrimination in the context of artificial intelligence (AI) systems have recently received increased attention from both legal and computer scie…
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
Hilde Weerts, Florian Pfisterer, Matthias Feurer +7
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducin…
Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning
Hilde Weerts, Lambèr Royakkers, Mykola Pechenizkiy
Fairness-aware machine learning (fair-ml) techniques are algorithmic interventions designed to ensure that individuals who are affected by the predictions of a machine learning mod…
An Introduction to Algorithmic Fairness
Hilde J. P. Weerts
In recent years, there has been an increasing awareness of both the public and scientific community that algorithmic systems can reproduce, amplify, or even introduce unfairness in…
Importance of Tuning Hyperparameters of Machine Learning Algorithms
Hilde J. P. Weerts, Andreas C. Mueller, Joaquin Vanschoren
The performance of many machine learning algorithms depends on their hyperparameter settings. The goal of this study is to determine whether it is important to tune a hyperparamete…
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
In many contexts, it can be useful for domain experts to understand to what extent predictions made by a machine learning model can be trusted. In particular, estimates of trustwor…
A Human-Grounded Evaluation of SHAP for Alert Processing
Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy
In the past years, many new explanation methods have been proposed to achieve interpretability of machine learning predictions. However, the utility of these methods in practical a…