Artificial intelligence, machine learning, and big data: Improvements to the science of people at work and applications to practice

被引:0
|
作者
Woo, Sang Eun [1 ,3 ]
Tay, Louis [1 ]
Oswald, Frederick [2 ]
机构
[1] Purdue Univ, Dept Psychol Sci, W Lafayette, IN USA
[2] Rice Univ, Dept Psychol Sci, Houston, TX USA
[3] Purdue Univ, Dept Psychol Sci, 703 Third St, W Lafayette, IN 47907 USA
关键词
artificial intelligence; big data; machine learning; ORGANIZATIONAL RESEARCH; SELF-REPORTS; RECOMMENDATIONS; PSYCHOLOGY; MANAGEMENT; BUSINESS; VALIDITY; PROGRESS; FIELD;
D O I
10.1111/peps.12643
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Currently, in the organizational research community, artificial intelligence (AI), machine learning (ML), and big data techniques are being vigorously explored as a set of modern-day approaches contributing to a multidisciplinary science of people at work. This paper discusses more specifically how these sophisticated technologies, methods, and data might together advance the science of people at work through various routes, including improving theory and knowledge, construct measurements, and predicting real-world outcomes. Inspired by the four articles in the current special issue highlighting several of these aspects in essential ways, we also share other possibilities for future organizational research. In addition, we indicate many key practical, ethical, and institutional challenges with research involving AI/ML and big data (i.e., data accessibility, methodological skill gaps, data transparency, privacy, reproducibility, generalizability, and interpretability). Taken together, the opportunities and challenges that lie ahead in the areas of AI and ML promise to reshape organizational research and practice in many exciting and impactful ways.
引用
收藏
页数:16
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