Defining bias in AI-systems: Biased models are fair models
Journal:
arXiv
Published Date:
Feb 25, 2025
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.
