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：优化被作为一种客观方法提出，用于解决涉及不确定性和利益冲突的复杂现实决策。它驱动着商业战略和公共政策，并日益成为复杂机器学习系统的核心。作为处理潜在高风险决策的一种范式，优化依赖于将现实世界抽象为一组决策、目标和约束。本文基于建模过程和一系列实际案例，描述了运用优化时必然涉及的规范性选择和假设。继而识别出可能被忽视的六类新兴问题：1) 错误指定的价值可能导致优化遗漏某些必要的指令，或将其错误地作为约束条件或目标的一部分纳入；2) 有问题的决策边界可能导致错误模块化假设及反馈循环；3) 未能考虑多个主体的分歧目标与决策可能导致政策仅服务于特定狭隘利益；4) 错误标注与测量误差可能引入偏差与不精确性；5) 缺乏正式刻画与保证的松弛和近似方法的不当使用会严重制约适用性；6) 在未指定必要语境信息的情况下，将优化视为行动正当性依据可能导致伦理上有争议或错误的决策。本文还提出了进一步理解并遏制优化不当使用所造成危害的建议。