系统化梳理机器学习中模型选择的任意性，揭示其背后原理与影响。
Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning
- 定义模型设计选择如何导致决策任意性
- 拓展多重性概念，涵盖预测与解释之外的维度
- 区分多重性、不确定性与方差，明确研究边界
算法建模依赖数据中的有限信息来推断未知场景的结果，常在决策中嵌入一定程度的任意性。近年来，多重性（multiplicity）这一视角受到关注，即研究一组‘良好模型’（likely to be deployed in practice）之间的任意性差异。本文系统化梳理该领域文献，具体包括：(a) 明确定义模型设计选择及其对任意性的贡献；(b) 扩展多重性定义，纳入预测与解释之外的未被充分重视形式；(c) 清晰区分多重性与其他任意性视角，如不确定性与方差；(d) 提炼多重性的收益与潜在风险，归纳为总体趋势，并将其置于负责任AI的宏观背景中。最后，提出开放研究问题，指出该新兴快速发展的研究方向中的前沿趋势。
原文摘要 · Abstract (English)
Algorithmic modeling relies on limited information in data to extrapolate outcomes for unseen scenarios, often embedding an element of arbitrariness in its decisions. A perspective on this arbitrariness that has recently gained interest is multiplicity-the study of arbitrariness across a set of "good models", i.e., those likely to be deployed in practice. In this work, we systemize the literature on multiplicity by: (a) formalizing the terminology around model design choices and their contribution to arbitrariness, (b) expanding the definition of multiplicity to incorporate underrepresented forms beyond just predictions and explanations, (c) clarifying the distinction between multiplicity and other lenses of arbitrariness, i.e., uncertainty and variance, and (d) distilling the benefits and potential risks of multiplicity into overarching trends, situating it within the broader landscape of responsible AI. We conclude by identifying open research questions and highlighting emerging trends in this young but rapidly growing area of research.
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