当AI学习共情：心理学视角下共情计算的主题、场景与优化

doi:10.3724/SP.J.1042.2024.00845

摘要/Abstract

摘要： 共情计算是指使用人工智能和大数据技术来预测、识别、模拟和生成人类的共情, 是传统心理学共情研究与计算机科学交叉的新兴研究领域。本研究构建了一个数据层、模型层与任务层的普适性研究框架, 总结了一个包括个体共情测评、共情内容分类、共情回应系统和共情对话生成的4个新主题分析框架, 建立了面向心理健康、教育学习、商业服务和公共管理等心理应用的场景创新。未来研究有必要建构高整合的共情计算理论模型、建设高可信的共情心理行为特征数据集, 并通过以人为中心的评价体系验证并改进共情计算的研究效度。共情计算有益于将当前人际共情研究扩展到智能社会新型人-AI关系研究中。心理学家在该领域承担引领、评估和改进等角色, 并与计算机科学家紧密合作, 共同推动共情计算理论基础更坚实, 效果检验更可靠, 应用创新更贴近实际需求。

关键词: 共情, 共情计算, 计算心理学, 人工智能, 人机交互

Abstract: Empathy computing is an emerging research field that integrates artificial intelligence (AI) and big data technology to predict, identify, simulate, and generate human empathy. This field builds upon psychological studies in terms of concepts, measurements, neural foundations, and applications of empathy, and employs innovative computing approaches for analyzing and simulating empathy. This article critically reviews current research on empathy computing and discusses its future directions from a psychological perspective, with the aim of facilitating foundational research and practical applications in this field.
The current research on empathy computing can be categorized into four themes based on different purposes and methods. On one hand, empathy computing primarily aims to analyze and comprehend empathy using computers. This endeavor can be further divided into two categories: (1) individual empathy assessment, which focuses on analyzing individual empathetic traits, and (2) empathetic content classification, which focuses on analyzing empathetic features in texts rather than individuals. On the other hand, research also focuses on simulating and expressing empathy through computing, which includes (3) the design of empathetic response systems and (4) the development of generative empathetic dialogue systems. The former provides users with a limited number of predefined rule-based responses and feedback to express empathy, while the latter utilizes AI to automatically generate a wide range of empathetic dialogues without relying on predefined rules. These four research streams are relatively independent yet complementary. Moreover, as research progresses, new directions will continue to emerge, such as improving the empathic capabilities of computers through brain-computer interface technology.
Although research on empathy computing is still in its early stages, it has shown potential for innovative applications in scenarios such as mental health, education, business services, and public management. With the increasing prevalence of artificial intelligence, these fields, which involve substantial interpersonal interactions, are positioned to become the primary domains for human-computer interaction. As a result, they emerge as the key application scenarios for empathy computing. In the realm of mental health, empathy computing can assist in automatically evaluating and enhancing therapists' empathetic abilities. Additionally, it can provide personalized empathetic support and guidance through AI-driven chatbots. In the field of education, empathy computing can facilitate the learning process by employing empathetic AI tutors. Within the business sector, it enables organizations to deliver tailored customer experiences, thereby enhancing satisfaction and fostering loyalty through the generation of empathic dialogues. In public management, empathy computing can be used to generate empathetic discourse to counteract negative speech. Additionally, it facilitates policymakers to respond empathetically to citizens' needs and inquiries, thereby fostering trust between the government and the public. These four scenarios illustrate the vast potential applications of empathy computing. However, due to concerns related to safety and ethics, complete reliance on computers to perform empathetic tasks is currently not feasible. Instead, a collaboration between humans and computers is necessary.
Empathy computing represents a transformative frontier, not only providing methods to measure and analyze empathy automatically on a larger scale but also enriching the theoretical landscape of empathy research. It extends traditional studies on empathy in interpersonal relationships to explore its emerging manifestations in human-AI relationships. This expansion raises novel questions about the universality of empathy and its potential evolution in human-computer interaction. Empathy computing holds the promise of serving as a cornerstone for a unified theory of empathy that encompasses diverse relationship dynamics, ranging from human-human to human-machine interactions and beyond. It is beneficial for comprehensively understanding empathy and effectively promoting it in the context of an intelligent society.
Future research should focus on developing integrated theoretical models of empathy computing, establishing reliable psychological and behavioral datasets of empathy-related characteristics, and validating and refining empathy computing research through a human-centered approach. Psychologists play indispensable roles in leading, evaluating, and optimizing research and practice in this field. The collaboration of scholars in psychology and computer science is imperative to ensure that AI learns empathy effectively and ethically, thereby fostering people’s wellbeing in the forthcoming intelligent society.

Key words: empathy, empathy computing, computational psychology, artificial intelligence, human-computer interaction

侯悍超, 倪士光, 林书亚, 王蒲生. (2024). 当AI学习共情：心理学视角下共情计算的主题、场景与优化. 心理科学进展 , 32(5), 845-858.

HOU Hanchao, NI Shiguang, LIN Shuya, WANG Pusheng. (2024). When AI learns to empathize: Topics, scenarios, and optimization of empathy computing from a psychological perspective. Advances in Psychological Science, 32(5), 845-858.

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