AI Biases as Asymmetries: A Review to Guide Practice
This work addresses the challenge of bias management for AI practitioners and researchers, offering a conceptual review that is incremental in nature.
The paper tackles the problem of understanding and managing bias in AI systems by proposing a framework that views biases as asymmetries, distinguishing three types and providing guidance on when to accept or mitigate them.
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.