The understanding of bias in AI is currently undergoing a revolution. Initially understood as errors or flaws, biases are increasingly recognized as integral to AI systems and sometimes preferable to less biased alternatives. In this paper, we review the reasons for this changed understanding and provide new guidance on two questions: First, how should we think about and measure biases in AI systems, consistent with the new understanding? Second, what kinds of bias in an AI system should we accept or even amplify, and what kinds should we minimize or eliminate, and why? The key to answering both questions, we argue, is to understand biases as "violations of a symmetry standard" (following Kelly). We distinguish three main types of asymmetry in AI systems-error biases, inequality biases, and process biases-and highlight places in the pipeline of AI development and application where bias of each type is likely to be good, bad, or inevitable.

本研究探讨了人工智能偏见的理解变化，提出了偏见不仅仅是错误，同时在某些情况下甚至可能优于其他较少偏见的选择。论文关键在于将偏见视为“对称标准的违反”，并分辨出三种主要的不对称类型，进而提出在人工智能系统中哪些偏见应被接受、放大或最小化、消除，以促进更有效的实践。

人工智能偏见作为不对称性：指导实践的综述