AI 中文总结
研究自然语言中效价能否反映道德，提出道德效价数据集，由六名参与者对常识规范库文本场景进行500次标注，效价范围-1到1，结果表明效价特征与道德类别相关，为文本道德估计有用性提供早期证据，推动情感道德计算研究。
AI 中文摘要
当前人工智能伦理的实现没有充分考虑情感。若人工智能应与人类伦理保持一致，那么研究反映人类道德行为的人工智能行为的可能性是合理的，因为情感在人类行为、判断或陈述中起作用。规范伦理学的主要理论在某种程度上都考虑人类情感，道德基础理论更是将情感视为核心。本文提出了一个道德效价数据集，由六名参与者对来自常识规范库数据集中文本场景的行动/判断和后果道德效价进行500次标注，效价范围为-1到1。结果表明效价特征与多类（不道德/任意/道德）和二元不道德/道德类别有显著关系，使用正则化逻辑回归进行二元分类时马修斯相关系数为0.764，这为效价特征用于文本道德估计的有用性提供了早期证据，表明考虑对他人反应的效价后果有助于构建更符合人类道德的人工智能。为促进进一步的情感道德计算研究，本研究的标注将可供索取。
英文摘要
Present implementations of artificial intelligence (AI) ethics do not adequately take feelings, or affect, into account. If AI should be aligned with human ethics, it seems reasonable to thoroughly investigate the possibility of AI behaviour that mirrors virtuous human ethical conduct, where feelings play a role in the actions, judgements or statements one makes. Furthermore, while prominent theories of normative ethics are often discussed in terms of their differences and shortcomings, Virtue, Consequentialist, and Kantian Deontological ethics all share a common feature of considering human feeling to some degree while the popular descriptive ethics theory, Moral Foundations Theory, positions feelings as central to many of its foundations. Therefore, in the present paper, a data set of moral valence is proposed, consisting of 500 annotations by six human participants for both action/judgement and consequence moral valence, ranging from -1 to 1 for text-presented scenarios from the Commonsense Norm Bank data set. The resulting valence features share significant relationships with multi-class (immoral/discretionary/moral) and binary immoral/moral categories while additionally providing a noteworthy test set Matthew's correlation coefficient of 0.764 using regularised logistic regression for binary classification. This provides early evidence of the usefulness of valence features for morality estimation of text, indicating that valenced consequences of responses for others can be considered toward more human morally-aligned AI. In the interest of promoting further affective-moral computing research, this study's annotations will be made available for research on request.
Comments8 pages, 2 figures, submitted to the 36th Irish Signals and Systems Conference
