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

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 …

As a highly data-driven application, recommender systems could be affected by data bias,
resulting in unfair results for different data groups, which could be a reason that affects the …

Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions
that have far‐reaching impact on individuals and society. Their decisions might affect …

We present a framework for quantifying and mitigating algorithmic bias in mechanisms
designed for ranking individuals, typically used as part of web-scale search and …

Recently, there has been a rising awareness that when machine learning (ML) algorithms
are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or …

Recommender systems are gaining increasing and critical impacts on human and society
since a growing number of users use them for information seeking and decision making …

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 …

Algorithmic decision making systems are ubiquitous across a wide variety of online as well
as offline services. These systems rely on complex learning methods and vast amounts of …

As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …