  AI-based systems are widely employed nowadays to make decisions that have
far-reaching impacts on individuals and society. Their decisions might affect
everyone, everywhere and anytime, entailing concerns about potential human
rights issues. Therefore, it is necessary to move beyond traditional AI
algorithms optimized for predictive performance and embed ethical and legal
principles in their design, training and deployment to ensure social good while
still benefiting from the huge potential of the AI technology. The goal of this
survey is to provide a broad multi-disciplinary overview of the area of bias in
AI systems, focusing on technical challenges and solutions as well as to
suggest new research directions towards approaches well-grounded in a legal
frame. In this survey, we focus on data-driven AI, as a large part of AI is
powered nowadays by (big) data and powerful Machine Learning (ML) algorithms.
If otherwise not specified, we use the general term bias to describe problems
related to the gathering or processing of data that might result in prejudiced
decisions on the bases of demographic features like race, sex, etc.
