Scruples：基于32，000个真实生活轶事的社区道德判断语料库
Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes
摘要：
随着人工智能系统成为人们日常生活中越来越重要的一部分，它们理解人们的道德规范变得越来越重要。出于描述性伦理学的动机，一个专注于人们的描述性判断而不是道德理论处方的研究领域，我们研究了一种新颖的，数据驱动的机器伦理学方法。我们介绍Scruples，这是第一个大规模的数据集，包含625，000个道德判断和32，000个真实生活中的轶事。每一个轶事都叙述了一个复杂的伦理状况，经常提出道德困境，并与社区成员贡献的判断分布相结合。我们的数据集对最先进的神经语言模型提出了重大挑战，留下了巨大的改进空间。然而，当呈现简化的道德情境时，结果更有希望，这表明神经模型可以有效地学习更简单的道德构建模块。我们的实证分析的一个关键结论是，规范并不总是明确的;许多情况下是自然分裂的。我们提出了一种新的方法来估计这些任务的最佳性能与固有的不同标签分布，并探讨似然函数，从模型的不确定性分离的内在。
英文摘要：
As AI systems become an increasing part of people's everyday lives, it becomes ever more important that they understand people's ethical norms. Motivated by descriptive ethics, a field of study that focuses on people's descriptive judgments rather than theoretical prescriptions on morality, we investigate a novel, data-driven approach to machine ethics. We introduce Scruples, the first large-scale dataset with 625,000 ethical judgments over 32,000 real-life anecdotes. Each anecdote recounts a complex ethical situation, often posing moral dilemmas, paired with a distribution of judgments contributed by the community members. Our dataset presents a major challenge to state-of-the-art neural language models, leaving significant room for improvement. However, when presented with simplified moral situations, the results are considerably more promising, suggesting that neural models can effectively learn simpler ethical building blocks. A key take-away of our empirical analysis is that norms are not always clean-cut; many situations are naturally divisive. We present a new method to estimate the best possible performance on such tasks with inherently diverse label distributions, and explore likelihood functions that separate intrinsic from model uncertainty.
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