{"title":"Artificial intelligence, machine learning, and big data: Improvements to the science of people at work and applications to practice","authors":"Sang Eun Woo, Louis Tay, Frederick Oswald","doi":"10.1111/peps.12643","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":48408,"journal":{"name":"Personnel Psychology","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Personnel Psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1111/peps.12643","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
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.
目前,在组织研究界,人工智能(AI)、机器学习(ML)和大数据技术正作为一套现代方法被积极探索,以促进关于工作中的人的多学科科学。本文将更具体地讨论这些先进的技术、方法和数据如何通过各种途径,包括改进理论和知识、构建测量和预测现实世界的结果,共同推进工作中的人这一科学。本期特刊中的四篇文章从本质上强调了上述几个方面,受此启发,我们也分享了未来组织研究的其他可能性。此外,我们还指出了涉及人工智能/移动语言和大数据的研究在实践、伦理和制度方面所面临的许多关键挑战(即数据可获取性、方法论技能差距、数据透明度、隐私、可重现性、可推广性和可解释性)。总之,人工智能和 ML 领域未来的机遇和挑战有望以多种令人兴奋、影响深远的方式重塑组织研究和实践。
期刊介绍:
Personnel Psychology publishes applied psychological research on personnel problems facing public and private sector organizations. Articles deal with all human resource topics, including job analysis and competency development, selection and recruitment, training and development, performance and career management, diversity, rewards and recognition, work attitudes and motivation, and leadership.