  With the widespread and pervasive use of Artificial Intelligence (AI) for
automated decision-making systems, AI bias is becoming more apparent and
problematic. One of its negative consequences is discrimination: the unfair, or
unequal treatment of individuals based on certain characteristics. However, the
relationship between bias and discrimination is not always clear. In this
paper, we survey relevant literature about bias and discrimination in AI from
an interdisciplinary perspective that embeds technical, legal, social and
ethical dimensions. We show that finding solutions to bias and discrimination
in AI requires robust cross-disciplinary collaborations.
