Chi-Ching Juan

Chi-Ching Juan, Tao Wang, Harold Lee

The appropriateness of empathy in AI has emerged as a critical concern, as excessive empathy risks seeming manipulative while insufficient empathy appears dismissive. While prior research has explored how to quantify empathy in AI, few studies examine whether such empathy is contextually appropriate. This paper introduces an economic perspective by applying signaling theory to human-AI conversations. We propose Signal Cost Proxies (emotional richness, perspective-taking, and contextual tailoring) mapped to affective, cognitive, and associative empathy. This multidimensional framework enables systematic evaluation of empathy not just by presence, but by its appropriateness relative to user demand.

Tao Wang, Chi-Ching Juan

A central challenge in affective computing is determining appropriate empathy levels for different interaction contexts. Prior work has characterized two poles: task-focused interactions, where empathy demand is near zero, and emotional disclosure, where empathy demand is high. This paper identifies a distinct intermediate type, decision support under stress, in which a sender faces a consequential choice while experiencing emotional difficulty. We hypothesize that this type elicits an asymmetric empathy profile: empathy comparable to emotional disclosure but instrumentality comparable to task-focused exchange. We test five hypotheses using 28,239 post-reply dyads from three Reddit advice communities, classified into three interaction types and scored for empathy depth, empathy form, and instrumental proportion using LLM-based annotation with pattern-based robustness checks. Results confirm the predicted asymmetric profile: decision-support-under-stress replies show significantly higher empathy than task-focused replies (M = 0.47 vs. 0.24, p < 0.001) while maintaining high instrumentality (0.83 vs. 0.77 for emotional disclosure, p < 0.001). Behavioral empathy dominates (36.6%), and community-validated response quality is negatively associated with empathic expression (r = -0.075, p < 0.001). Community norms modulate baselines substantially but preserve the structural ordering. These findings establish a human empathy baseline for this interaction type and have direct implications for calibrating empathic expression in affective AI systems.