“向善”且“为善”：多模态情感识别的伦理约束研究

“Striving for Good and Acting for Good”: A Study on Ethical Constraints in Multimodal Emotion Recognition

摘要: 多模态情感识别作为人工智能情感计算领域的前沿方向，其技术融合了计算机视觉、语音处理、生理信号分析等多种模态，显著提升了情感识别的准确性与鲁棒性。然而，技术能力的跃升也带来了伦理风险的质变——从传统AI的“行为观察”迈向对个体内在情感状态的深度识别，对精神隐私、情感自主性乃至人类主体性构成深层挑战。本研究系统剖析了多模态情感识别的技术特性与伦理风险，指出其具有学科交叉复杂性、伦理碰撞冲突性、善恶影响非对称性等新特点，并在数据隐私、算法歧视、情感操控、人性异化四个维度展开风险分析。在此基础上，基于《关于加强科技伦理治理的意见》与《人工智能伦理治理标准化指南》，建构了“5 + 10 + X”的伦理风险评价准则体系。进一步引入海德格尔“共在”思想、梅洛–庞蒂身体现象学与马克思异化理论，尝试构建“哲学 + 技术”深度融合的治理框架，为多模态情感识别的伦理约束与制度规范提供理论支撑与实践路径。

Abstract: As a cutting-edge direction in the field of affective computing within artificial intelligence, multimodal emotion recognition integrates various modalities such as computer vision, speech processing, and physiological signal analysis, significantly enhancing the accuracy and robustness of emotion recognition. However, this leap in technological capability also brings about a qualitative shift in ethical risks—moving from the “observation of behavior” characteristic of traditional AI to the “penetration of the inner self,” posing profound challenges to mental privacy, emotional autonomy, and even human subjectivity. This study systematically analyzes the technological characteristics and ethical risks of multimodal emotion recognition, identifying new features such as the complexity of interdisciplinary integration, the clash of ethical conflicts, and the asymmetry between beneficial and harmful impacts. It further examines risks across four dimensions: data privacy, algorithmic discrimination, emotional manipulation, and human alienation. On this basis, drawing from the “Opinions on Strengthening Ethical Governance in Science and Technology” and the “Guidelines for Ethical Governance in Artificial Intelligence Standardization”, this study constructs a “5 + 10 + X” ethical risk evaluation criteria system. Additionally, by incorporating Heidegger’s concept of “Being-with” (Mitsein), Merleau-Ponty’s phenomenology of the body, and Marx’s theory of alienation, this study attempts to develop a governance framework that deeply integrates philosophy and technology, providing theoretical support and practical pathways for the ethical constraints and institutional regulation of multimodal emotion recognition.