Optimization is offered as an objective approach to resolving complex, real-world decisions involving uncertainty and conflicting interests. It drives business strategies as well as public policies and, increasingly, lies at the heart of sophisticated machine learning systems. A paradigm used to approach potentially high-stakes decisions, optimization relies on abstracting the real world to a set of decision(s), objective(s) and constraint(s). Drawing from the modeling process and a range of actual cases, this paper describes the normative choices and assumptions that are necessarily part of using optimization. It then identifies six emergent problems that may be neglected: 1) Misspecified values can yield optimizations that omit certain imperatives altogether or incorporate them incorrectly as a constraint or as part of the objective, 2) Problematic decision boundaries can lead to faulty modularity assumptions and feedback loops, 3) Failing to account for multiple agents' divergent goals and decisions can lead to policies that serve only certain narrow interests, 4) Mislabeling and mismeasurement can introduce bias and imprecision, 5) Faulty use of relaxation and approximation methods, unaccompanied by formal characterizations and guarantees, can severely impede applicability, and 6) Treating optimization as a justification for action, without specifying the necessary contextual information, can lead to ethically dubious or faulty decisions. Suggestions are given to further understand and curb the harms that can arise when optimization is used wrongfully.

翻译：优化作为一种解决涉及不确定性和利益冲突的复杂现实决策的客观方法被提出。它驱动着商业战略和公共政策，并且日益成为复杂机器学习系统的核心。作为一种处理潜在高风险决策的范式，优化依赖于将现实世界抽象为一组决策、目标和约束。本文借鉴建模过程和一系列实际案例，描述了使用优化时必须包含的规范性选择和假设。随后，本文指出了六个可能被忽视的涌现性问题：1) 错误指定的价值可能导致优化忽略某些必要的指令，或将其错误地作为约束或目标的一部分纳入；2) 有问题的决策边界可能导致有缺陷的模块化假设和反馈循环；3) 未能考虑多个主体的不同目标和决策可能导致政策仅服务于某些狭隘利益；4) 错误标注和错误测量可能引入偏见和不精确性；5) 缺乏正式表征和保证的松弛与近似方法的错误使用，可能严重阻碍可应用性；6) 将优化视为行动的正当理由，而未指定必要的背景信息，可能导致伦理上有问题或有缺陷的决策。本文提出了进一步理解和遏制优化被错误使用时可能产生的危害的建议。