12 citations · 18 across the 6 of their papers we have counts for
4 papers · 1 filter
"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…