In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …

This paper provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

Non-discrimination is a recognized objective in algorithmic decision making. In this paper,
we introduce a novel probabilistic formulation of data pre-processing for reducing …

We summarize the potential impact that the European Union's new General Data Protection
Regulation will have on the routine use of machine learning algorithms. Slated to take effect …

In this work, we define and solve the Fair Top-k Ranking problem, in which we want to
determine a subset of k candidates from a large pool of n» k candidates, maximizing utility …

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers
trained on such datasets can inherit these biases. In this paper, we provide a mathematical …

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases
in historical data used to train them. While computational techniques are emerging to …

This paper defines software fairness and discrimination and develops a testing-based
method for measuring if and how much software discriminates, focusing on causality in …

When it is ethical and legal to use a sensitive attribute (such as gender or race) in machine
learning systems, the question remains how to do so. We show that the naive application of …