Abstract: “Value sensitivity” originates from Batya Friedman’s concept of “Value-Sensitive Design”, referring to the ability to perceive ethically relevant values during the design, development, production, and use of technology. The research team innovatively applied the “tripartite methodology” and used the Multiple Indicators and Multiple Causes (MIMIC) structural equation model to measure public value sensitivity toward artificial intelligence (AI) and assess whether the current technologies support value-sensitive design goals. The study found that: (1) Public sensitivity to AI values is ranked as transparency, algorithmic fairness, job security, and responsibility attribution, indicating that these values should be prioritized in the design stage; (2) Information security and ecological imbalance received less attention; (3) Gender and profession showed no significant impact on value sensitivity, but age and education level did show significant differences.

Key Words: Artificial intelligence; Value sensitivity; MIMIC; Ethical governance

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