Bias and Discrimination in AI: a cross-disciplinary perspective

TL;DR

This paper provides a comprehensive interdisciplinary survey of AI bias and discrimination, emphasizing the need for cross-disciplinary collaboration to develop effective solutions.

Contribution

It offers an integrated perspective combining technical, legal, social, and ethical dimensions to better understand and address bias and discrimination in AI systems.

Findings

Bias and discrimination are interconnected but not identical issues.

Cross-disciplinary approaches are essential for effective solutions.

A survey of literature highlights gaps and opportunities for collaboration.

Abstract

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.