Feature attribution for kernel methods is often heuristic and not individualised for each
prediction. To address this, we turn to the concept of Shapley values (SV), a coalition game …

A review of the main fairness definitions and fair learning methodologies proposed in the
literature over the last years is presented from a mathematical point of view. Following our …

This paper introduces the Fair Fairness Benchmark (\textsf {FFB}), a benchmarking
framework for in-processing group fairness methods. Ensuring fairness in machine learning …

Ensuring that a predictor is not biased against a sensitive feature is the goal of fair learning.
Meanwhile, Global Sensitivity Analysis (GSA) is used in numerous contexts to monitor the …

Bias in classifiers is a severe issue of modern deep learning methods, especially for their
application in safety-and security-critical areas. Often, the bias of a classifier is a direct …

The burgeoning field of Natural Language Processing (NLP) stands at a critical juncture
where the integration of fairness within its frameworks has become an imperative. This PhD …

We revisit widely used preferential Gaussian processes (PGP) by Chu and Ghahramani
[2005] and challenge their modelling assumption that imposes rankability of data items via …

Optimization hierarchy for fair statistical decision problems Page 1 The Annals of Statistics 2022,
Vol. 50, No. 6, 3144–3173 https://doi.org/10.1214/22-AOS2217 © Institute of Mathematical …

Knowledge distillation (KD) is a simple and successful method to transfer knowledge from a
teacher to a student model solely based on functional activity. However, it has recently been …

Deep neural networks have reached impressive performance in many tasks in computer
vision and its applications. However, research into understanding deep neural networks is …