论文标题
顾虑：32,000个现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
论文作者
论文摘要
随着AI系统成为人们日常生活的越来越多的一部分，他们了解人们的道德准则变得越来越重要。由描述性伦理学的动机，一个研究领域，重点是人们的描述性判断而不是关于道德的理论规定，我们研究了一种新颖的，数据驱动的机器伦理方法。 我们介绍了第一个大规模数据集，具有625,000个道德判断，超过32,000个现实生活中的轶事。每个轶事都讲述了一个复杂的道德局势，通常会构成道德困境，并与社区成员贡献的判决分布相结合。我们的数据集对最先进的神经语言模型提出了一个重大挑战，留下了很大的改进空间。但是，当表现出简化的道德情况时，结果将变得更加有前途，这表明神经模型可以有效地学习更简单的道德构件。 我们的经验分析的关键是，规范并不总是干净的。许多情况自然是分裂的。我们提出了一种新方法，以估计具有本质上不同标签分布的此类任务的最佳性能，并探索将本质与模型不确定性分开的似然函数。
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.
