AI ethics is an emerging field with multiple, competing narratives about how to best solve the problem of building human values into machines. Two major approaches are focused on bias and compliance, respectively. But neither of these ideas fully encompasses ethics: using moral principles to decide how to act in a particular situation. Our method posits that the way data is labeled plays an essential role in the way AI behaves, and therefore in the ethics of machines themselves. The argument combines a fundamental insight from ethics (i.e. that ethics is about values) with our practical experience building and scaling machine learning systems. We want to build AI that is actually ethical by first addressing foundational concerns: how to build good systems, how to define what is good in relation to system architecture, and who should provide that definition. Building ethical AI creates a foundation of trust between a company and the users of that platform. But this trust is unjustified unless users experience the direct value of ethical AI. Until users have real control over how algorithms behave, something is missing in current AI solutions. This causes massive distrust in AI, and apathy towards AI ethics solutions. The scope of this paper is to propose an alternative path that allows for the plurality of values and the freedom of individual expression. Both are essential for realizing true moral character.

翻译：AI伦理是一个新兴领域，关于如何将人类价值融入机器这一最佳解决方案存在多种相互竞争的叙事。两大主要方法分别聚焦于偏见与合规。然而，这两种理念均未能完全涵盖伦理的内涵：即运用道德原则来指导在特定情境下如何行动。我们的方法认为，数据标注的方式在AI的行为塑造中起着关键作用，进而影响机器自身的伦理体系。这一论点融合了伦理学的基本洞见（即伦理关乎价值）与我们在构建和扩展机器学习系统中的实践经验。我们希望通过首先解决基础性问题来构建真正符合伦理的AI：如何构建优良的系统、如何根据系统架构定义“优良”，以及由谁来提供该定义。构建合乎伦理的AI为公司与平台用户之间建立了信任基础。但除非用户能直接体验到伦理AI的价值，否则这种信任便缺乏依据。在当前的AI解决方案中，用户对算法行为缺乏真正控制权，这导致对AI的普遍不信任以及对AI伦理解决方案的冷漠。本文旨在提出一条替代路径，该路径允许多元化价值的存在与个体表达的自由——这两者对于实现真正的道德特质均至关重要。