50 citations · 202 across the 13 of their papers we have counts for
17 papers
QUACKIE: A NLP Classification Task With Ground Truth Explanations
Yves Rychener, Xavier Renard, Djamé Seddah +2
NLP Interpretability aims to increase trust in model predictions. This makes evaluating interpretability approaches a pressing issue. There are multiple datasets for evaluating NLP…
Active Fairness Instead of Unawareness
Boris Ruf, Marcin Detyniecki
The possible risk that AI systems could promote discrimination by reproducing and enforcing unwanted bias in data has been broadly discussed in research and society. Many current l…
Learning Unbiased Representations via Rényi Minimization
Vincent Grari, Oualid El Hajouji, Sylvain Lamprier +1
In recent years, significant work has been done to include fairness constraints in the training objective of machine learning algorithms. Many state-of the-art algorithms tackle th…
Adversarial Learning for Counterfactual Fairness
Vincent Grari, Sylvain Lamprier, Marcin Detyniecki
In recent years, fairness has become an important topic in the machine learning research community. In particular, counterfactual fairness aims at building prediction models which…
Vehicle Telematics Via Exteroceptive Sensors: A Survey
Fernando Molano Ortiz, Matteo Sammarco, Luís Henrique M. K. Costa +1
Whereas a very large number of sensors are available in the automotive field, currently just a few of them, mostly proprioceptive ones, are used in telematics, automotive insurance…
Getting Fairness Right: Towards a Toolbox for Practitioners
Boris Ruf, Chaouki Boutharouite, Marcin Detyniecki
The potential risk of AI systems unintentionally embedding and reproducing bias has attracted the attention of machine learning practitioners and society at large. As policy makers…