Machine ethics ensures ethical conduct in Artificial Intelligence (AI) models and agents. Examining real-life applications benefit learning practical ethics in many situations, offering valuable data to grasp the complexities of human ethics in diverse contexts. In this paper, we examine social media platforms for understanding real-life ethical scenarios and human moral judgments. We examine posts from a popular Reddit subreddit (i.e., a subcommunity) called r/AmITheAsshole, where authors and commenters share their moral judgments on who is blameworthy. We employ computational techniques to investigate the underlying reasoning influencing moral judgments. We focus on excerpts-which we term moral sparks-from original posts that commenters include to indicate what motivates their judgments. To this end, we examine how (1) events activating social commonsense and (2) linguistic signals affect moral sparks assignment and their subsequent judgments. By examining over 24 672 posts and 175988 comments, we find that event-related negative character traits (e.g., immature and rude) attract attention and stimulate blame, implying a dependent relationship between character traits and moral values. Specially, we focus on causal graph involving events (c-events) that activate social commonsense. We observe that c-events are perceived with varying levels of informativeness, influencing moral spark and judgment assignment in distinct ways. This observation is reinforced by examining linguistic features describing semantically similar c-events. Moreover, language influencing commenters' cognitive processes enhances the probability of an excerpt becoming a moral spark, while factual and concrete descriptions tend to inhibit this effect.
翻译:暂无翻译