论文标题
机器学习方法在自然发生的故事中进行原理预测
Machine Learning Approaches for Principle Prediction in Naturally Occurring Stories
论文作者
论文摘要
价值对齐是创建自主系统的任务，其价值与人类的价值保持一致。过去的工作表明，故事是有关人类价值观的潜在丰富信息来源。但是，过去的工作仅限于考虑二进制意义的价值。在这项工作中，我们探讨了机器学习模型在自然发生的故事数据上的规范原理预测任务。为此，我们扩展了一个以前用于培训具有道德原则注释的二进制规范分类器的数据集。然后，我们使用此数据集训练各种机器学习模型，评估这些模型并将其结果与被要求执行相同任务的人进行比较。我们表明，尽管可以对个人原则进行分类，但“道德原则”所代表的内容的歧义对人类参与者和面临同一任务的自治系统提出了挑战。
Value alignment is the task of creating autonomous systems whose values align with those of humans. Past work has shown that stories are a potentially rich source of information on human values; however, past work has been limited to considering values in a binary sense. In this work, we explore the use of machine learning models for the task of normative principle prediction on naturally occurring story data. To do this, we extend a dataset that has been previously used to train a binary normative classifier with annotations of moral principles. We then use this dataset to train a variety of machine learning models, evaluate these models and compare their results against humans who were asked to perform the same task. We show that while individual principles can be classified, the ambiguity of what "moral principles" represent, poses a challenge for both human participants and autonomous systems which are faced with the same task.
