12 citations · 18 across the 6 of their papers we have counts for
7 papers
No evaluation without fair representation : Impact of label and selection bias on the evaluation, performance and mitigation of classification models
Magali Legast, Toon Calders, François Fouss
Bias can be introduced in diverse ways in machine learning datasets, for example via selection or label bias. Although these bias types in themselves have an influence on important…
Would a Large Language Model Pay Extra for a View? Inferring Willingness to Pay from Subjective Choices
Manon Reusens, Sofie Goethals, Toon Calders +1
As Large Language Models (LLMs) are increasingly deployed in applications such as travel assistance and purchasing support, they are often required to make subjective choices on be…
Interpretable and Fair Mechanisms for Abstaining Classifiers
Daphne Lenders, Andrea Pugnana, Roberto Pellungrini +3
Abstaining classifiers have the option to refrain from providing a prediction for instances that are difficult to classify. The abstention mechanism is designed to trade off the cl…
"Patriarchy Hurts Men Too." Does Your Model Agree? A Discussion on Fairness Assumptions
Marco Favier, Toon Calders
The pipeline of a fair ML practitioner is generally divided into three phases: 1) Selecting a fairness measure. 2) Choosing a model that minimizes this measure. 3) Maximizing the m…
Cherry on the Cake: Fairness is NOT an Optimization Problem
Marco Favier, Toon Calders
In Fair AI literature, the practice of maliciously creating unfair models that nevertheless satisfy fairness constraints is known as "cherry-picking". A cherry-picking model is a m…
How to be fair? A study of label and selection bias
Marco Favier, Toon Calders, Sam Pinxteren +1
It is widely accepted that biased data leads to biased and thus potentially unfair models. Therefore, several measures for bias in data and model predictions have been proposed, as…
Reranking individuals: The effect of fair classification within-groups
Sofie Goethals, Marco Favier, Toon Calders
Artificial Intelligence (AI) finds widespread application across various domains, but it sparks concerns about fairness in its deployment. The prevailing discourse in classificatio…