23 citations · 81 across the 22 of their papers we have counts for
8 papers · 1 filter
Fairness without the sensitive attribute via Causal Variational Autoencoder
Vincent Grari, Sylvain Lamprier, Marcin Detyniecki
In recent years, most fairness strategies in machine learning models focus on mitigating unwanted biases by assuming that the sensitive information is observed. However this is not…
How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice
Tom Vermeire, Thibault Laugel, Xavier Renard +2
Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Vari…
Understanding surrogate explanations: the interplay between complexity, fidelity and coverage
Rafael Poyiadzi, Xavier Renard, Thibault Laugel +2
This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings. We start our exposition by considering glob…
On the overlooked issue of defining explanation objectives for local-surrogate explainers
Rafael Poyiadzi, Xavier Renard, Thibault Laugel +2
Local surrogate approaches for explaining machine learning model predictions have appealing properties, such as being model-agnostic and flexible in their modelling. Several method…
Explaining how your AI system is fair
Boris Ruf, Marcin Detyniecki
To implement fair machine learning in a sustainable way, choosing the right fairness objective is key. Since fairness is a concept of justice which comes in various, sometimes conf…
Implementing Fair Regression In The Real World
Boris Ruf, Marcin Detyniecki
Most fair regression algorithms mitigate bias towards sensitive sub populations and therefore improve fairness at group level. In this paper, we investigate the impact of such impl…
Understanding Prediction Discrepancies in Machine Learning Classifiers
Xavier Renard, Thibault Laugel, Marcin Detyniecki
A multitude of classifiers can be trained on the same data to achieve similar performances during test time, while having learned significantly different classification patterns. T…
Towards the Right Kind of Fairness in AI
Boris Ruf, Marcin Detyniecki
Fairness is a concept of justice. Various definitions exist, some of them conflicting with each other. In the absence of an uniformly accepted notion of fairness, choosing the righ…