定义AI系统中的偏见：有偏模型即公平模型
Defining bias in AI-systems: Biased models are fair models
AI总结：
本文挑战无偏模型即公平模型的假设，主张精确定义偏见，区分偏见与歧视，以促进AI公平性讨论。
摘要：
围绕AI系统中偏见的争论是算法公平性讨论的核心。然而，“偏见”一词往往缺乏明确的定义，尽管它经常与公平性相对照，暗示无偏模型本质上是公平的。在本文中，我们挑战这一假设，并认为精确的概念化偏见对于有效解决公平性问题至关重要。我们并非将偏见视为固有负面或不公平的，而是强调区分偏见与歧视的重要性。我们进一步探讨这种关注点的转变如何能在AI系统公平性的学术辩论中促进更具建设性的讨论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
