为AI建立心理测量学，用科学方法评估其行为背后的深层特质。
Machine Psychometrics: A Mathematical Psychology of Artificial Intelligence
- 构建机器心理画像（Machine Mindprint），量化评估AI的自我认知、抗干扰等多维能力。
- 提出可信度协议，通过探测测试与长期监控实现高风险场景下的安全部署。
- 避开意识争议，以测量代替判断，适合对AI行为可靠性有严格要求的领域。
当前人工智能生成的行为已足够丰富，引发信任、惊喜与担忧，但现有评估工具仍偏重能力分数，忽视心理结构。本文提出机器心理测量学（Machine Psychometrics），基于认知连续体理论与数学心理学方法（项目反应理论、信号检测理论、贝叶斯认知建模等），构建一套针对人工代理的隐性行为、元认知、沟通与自我建模倾向的测量体系。核心是机器心智指纹（Machine Mindprint），一个涵盖校准度、源完整性、抗暗示性、情境稳定性、表达一致性、工具完整性、漂移监测与分布基础的多维、领域限定、版本化画像。配套的信任协议通过探针电池、扰动测试、信效度分析及纵向监控，将心智指纹转化为高风险场景下的部署决策。哲学上提出第三立场——机器心智纪律，既不拟人化也不否定，既不预设意识也不排斥它。目标不是让AI像人，而是因它非人而精准理解，以测量先于判断。
原文摘要 · Abstract (English)
Artificial agents now generate behavior rich enough to invite trust, surprise, and concern, yet our evaluation tools still privilege capability scores over psychological structure. This paper argues that the philosophical impasse between two symmetrical errors (Artificial Mind Blindness, which dismisses psychological organization in non-biological systems, and Artificial Mind Projection, which infers human-like inner life from fluent behavior alone) can be circumvented not by resolving the consciousness question, but by introducing a disciplined measurement layer beneath it. Drawing on Michael Levin's continuum view of cognition as goal-directed competency across substrates, and on the methodological repertoire of mathematical psychology (Item Response Theory, Signal Detection Theory, Bayesian cognitive modeling, calibration analysis, cognitive-bias batteries), the paper develops Machine Psychometrics as a measurement science of latent behavioral, metacognitive, communicative, and self-modeling dispositions in artificial agents. Its operational core is the Machine Mindprint: a multidimensional, domain-bounded, versioned profile spanning calibration, source integrity, suggestibility resistance, context stability, expressive alignment, tool integrity, drift monitoring, and distributional grounding. A complementary Trust Protocol turns Mindprints into deployment decisions through probe batteries, perturbation testing, reliability and validity analysis, and longitudinal monitoring across high-stakes domains. The philosophical contribution is a third stance, Artificial Mind Discipline, that neither anthropomorphizes nor dismisses, neither presupposes consciousness nor forecloses it. The aim is not to humanize artificial agents, but to understand them precisely because they are not human, through measurement before judgment.
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