Psychological Measurement in the Information Age: Machine-Learned Computational Models
Psychological Measurement in the Information Age: Machine-Learned Computational Models
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DOI：
10.1177/09637214211056906
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发表时间：
2022-02-01
影响因子：
7.2
通讯作者：
Southwell, Rosy
中科院分区：
文献类型：
--
作者：
D'Mello, Sidney K.;Tay, Louis;Southwell, Rosy
心理科学可以从计算和信息科学的新兴方法中受益，并为这些方法做出贡献，这些方法由真实世界数据的可用性以及传感和计算的进步驱动。我们专注于这样一种方法，机器学习计算模型（MLCM）-从数据中学习的计算机程序，通常在人类监督下。我们介绍MLCM，并讨论他们如何与传统的计算模型和评估心理科学。从认知和情感科学，神经科学，教育，组织心理学，人格和社会心理学的MLCM的例子提供。我们认为MLCM为基础的措施的准确性和普遍性，提醒研究人员考虑的基本情况和预期用途时，解释他们的表现。我们的结论是，除了已知的数据隐私和安全问题，MLCM的使用需要重新定义公平性，偏见，可解释性和负责任的使用。
Psychological science can benefit from and contribute to emerging approaches from the computing and information sciences driven by the availability of real-world data and advances in sensing and computing. We focus on one such approach, machine-learned computational models (MLCMs)-computer programs learned from data, typically with human supervision. We introduce MLCMs and discuss how they contrast with traditional computational models and assessment in the psychological sciences. Examples of MLCMs from cognitive and affective science, neuroscience, education, organizational psychology, and personality and social psychology are provided. We consider the accuracy and generalizability of MLCM-based measures, cautioning researchers to consider the underlying context and intended use when interpreting their performance. We conclude that in addition to known data privacy and security concerns, the use of MLCMs entails a reconceptualization of fairness, bias, interpretability, and responsible use.