Defining bias in AI-systems: Biased models are fair models

TL;DR

This paper argues that understanding bias in AI requires a clear definition, distinguishing it from fairness and discrimination, to improve fairness discussions and model evaluation.

Contribution

It challenges the assumption that biased models are unfair, proposing a nuanced conceptualization of bias to advance fairness debates in AI.

Findings

Bias and fairness are distinct concepts in AI systems.

A clear definition of bias can improve fairness assessments.

Reframing bias helps foster more constructive academic discussions.

Abstract

The debate around bias in AI systems is central to discussions on algorithmic fairness. However, the term bias often lacks a clear definition, despite frequently being contrasted with fairness, implying that an unbiased model is inherently fair. In this paper, we challenge this assumption and argue that a precise conceptualization of bias is necessary to effectively address fairness concerns. Rather than viewing bias as inherently negative or unfair, we highlight the importance of distinguishing between bias and discrimination. We further explore how this shift in focus can foster a more constructive discourse within academic debates on fairness in AI systems.