  In this paper we examine algorithmic fairness from the perspective of law
aiming to identify best practices and strategies for the specification and
adoption of fairness definitions and algorithms in real-world systems and use
cases. We start by providing a brief introduction of current
anti-discrimination law in the European Union and the United States and
discussing the concepts of bias and fairness from an legal and ethical
viewpoint. We then proceed by presenting a set of algorithmic fairness
definitions by example, aiming to communicate their objectives to non-technical
audiences. Then, we introduce a set of core criteria that need to be taken into
account when selecting a specific fairness definition for real-world use case
applications. Finally, we enumerate a set of key considerations and best
practices for the design and employment of fairness methods on real-world AI
applications
