5 papers
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…
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…
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…
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…
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…