Algorithms learned from data are increasingly used for deciding many aspects in our life:
from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence …

Researchers and practitioners in the fairness community have highlighted the ethical and
legal challenges of using biased datasets in data-driven systems, with algorithmic bias …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

In recent years fairness in machine learning (ML) has emerged as a highly active area of
research and development. Most define fairness in simple terms, where fairness means …

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 …

Fairness and robustness in machine learning are crucial when individuals are subject to
automated decisions made by models in high-stake domains. To promote ethical artificial …

Machine learning and access to big data are revolutionizing the way many industries
operate, providing analytics and automation to many aspects of real-world practical tasks …

Trustworthy AI is becoming ever more important in both machine learning and legal
domains. One important consequence is that decision makers must seek to guarantee a'fair' …

We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

Past literature has been effective in demonstrating ideological gaps in machine learning
(ML) fairness definitions when considering their use in complex socio-technical systems …