论文标题
优化的标量目标价值不能是客观的，不应成为唯一的目标
An optimizable scalar objective value cannot be objective and should not be the sole objective
论文作者
论文摘要
本文涉及算法和计算系统的道德和道德，并且在过去几年中一直在Facebook内部传播。该论文回顾了许多诺贝尔奖获得者的作品，以及其他著名科学家，例如理查德·道金斯（Richard Dawkins），安德烈·科尔莫戈罗夫（Andrei Kolmogorov），维尔弗雷多·帕雷托（Vilfredo Pareto）和约翰·冯·诺伊曼（John von Neumann）。该论文根据标题中总结的基于此类作品得出结论。该论文认为，现代机器学习和人工智能的标准方法必然会偏见和不公平，并且法律，正义，政治和医学专业的长期传统应该有所帮助。
This paper concerns the ethics and morality of algorithms and computational systems, and has been circulating internally at Facebook for the past couple years. The paper reviews many Nobel laureates' work, as well as the work of other prominent scientists such as Richard Dawkins, Andrei Kolmogorov, Vilfredo Pareto, and John von Neumann. The paper draws conclusions based on such works, as summarized in the title. The paper argues that the standard approach to modern machine learning and artificial intelligence is bound to be biased and unfair, and that longstanding traditions in the professions of law, justice, politics, and medicine should help.
