Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management

IF 8.2 1区管理学 Q1 MANAGEMENT

Human Resource Management Review Pub Date : 2023-03-01 DOI:10.1016/j.hrmr.2021.100881

Markus Langer, Cornelius J. König

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引用次数: 8

Abstract

Artificial Intelligence and algorithmic technologies support or even automate a large variety of human resource management (HRM) activities. This affects a range of stakeholders with different, partially conflicting perspectives on the opacity and transparency of algorithm-based HRM. In this paper, we explain why opacity is a key characteristic of algorithm-based HRM, describe reasons for opaque algorithm-based HRM, and highlight the implications of opacity from the perspective of the main stakeholders involved (users, affected people, deployers, developers, and regulators). We also review strategies to reduce opacity and promote transparency of algorithm-based HRM (technical solutions, education and training, regulation and guidelines), and emphasize that opacity and transparency in algorithm-based HRM can simultaneously have beneficial and detrimental consequences that warrant taking a multi-stakeholder view when considering these consequences. We conclude with a research agenda highlighting stakeholders' interests regarding opacity, strategies to reduce opacity, and consequences of opacity and transparency in algorithm-based HRM.

查看原文本刊更多论文

在基于算法的人力资源管理中，引入多方利益相关者对不透明性、透明度和减少不透明性的策略的看法

人工智能和算法技术支持甚至自动化各种各样的人力资源管理(HRM)活动。这影响了一系列利益相关者，他们对基于算法的人力资源管理的不透明性和透明度有着不同的、部分冲突的观点。在本文中，我们解释了为什么不透明是基于算法的人力资源管理的一个关键特征，描述了基于算法的人力资源管理不透明的原因，并从涉及的主要利益相关者(用户、受影响的人、部署者、开发人员和监管机构)的角度强调了不透明的含义。我们还回顾了减少不透明度和提高基于算法的人力资源管理透明度的策略(技术解决方案、教育和培训、监管和指导方针)，并强调基于算法的人力资源管理的不透明度和透明度可以同时产生有益和有害的后果，因此在考虑这些后果时需要采取多利益相关者的观点。我们总结了一个研究议程，强调了利益相关者对不透明度的兴趣，减少不透明度的策略，以及基于算法的人力资源管理中不透明度和透明度的后果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Human Resource Management Review MANAGEMENT-

CiteScore

20.20

自引率

7.00%

发文量

审稿时长

48 days

期刊介绍： The Human Resource Management Review (HRMR) is a quarterly academic journal dedicated to publishing scholarly conceptual and theoretical articles in the field of human resource management and related disciplines such as industrial/organizational psychology, human capital, labor relations, and organizational behavior. HRMR encourages manuscripts that address micro-, macro-, or multi-level phenomena concerning the function and processes of human resource management. The journal publishes articles that offer fresh insights to inspire future theory development and empirical research. Critical evaluations of existing concepts, theories, models, and frameworks are also encouraged, as well as quantitative meta-analytical reviews that contribute to conceptual and theoretical understanding. Subject areas appropriate for HRMR include (but are not limited to) Strategic Human Resource Management, International Human Resource Management, the nature and role of the human resource function in organizations, any specific Human Resource function or activity (e.g., Job Analysis, Job Design, Workforce Planning, Recruitment, Selection and Placement, Performance and Talent Management, Reward Systems, Training, Development, Careers, Safety and Health, Diversity, Fairness, Discrimination, Employment Law, Employee Relations, Labor Relations, Workforce Metrics, HR Analytics, HRM and Technology, Social issues and HRM, Separation and Retention), topics that influence or are influenced by human resource management activities (e.g., Climate, Culture, Change, Leadership and Power, Groups and Teams, Employee Attitudes and Behavior, Individual, team, and/or Organizational Performance), and HRM Research Methods.