人工智能赋能人力资源管理研究综述： 理论框架、实践悖论与研究缺口

罗子敬

doi:10.70693/202607121395

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人工智能赋能人力资源管理研究综述：理论框架、实践悖论与研究缺口

Artificial Intelligence-Enabled Human Resource Management: Theoretical Perspectives, Practical Paradoxes and Open Research Questions

作者：罗子敬

单位：新余青云联合会计师事务所

卷期：2026年8月-2卷8期

通讯作者：罗子敬

DOI：10.70693/202607121395

页码：13-21

发布时间：2026-08-06

总浏览量：366

摘要

摘要：人工智能已经越过单纯的信息化辅助阶段，开始进入招聘甄选、培训开发、绩效评价、薪酬服务、员工关系与人才规划等管理环节。已有研究较充分地呈现了算法在信息处理、预测分析和个性化服务方面的潜力，但效率提升并不会自动带来公平、信任或更好的组织结果。本文在规范化检索的基础上，对2015—2026年中英文研究进行整合性述评，重点考察概念边界、研究演进、理论解释、应用情境及治理争议。梳理表明，相关研究的关注点经历了由流程电子化到预测分析、再到价值与责任治理的移动；国内成果偏重技术落地和组织转型，国外文献则较早讨论歧视、劳动控制、员工权利与问责。在此基础上，本文把技术、组织、个体、价值和结果纳入同一分析链条，并从实际管理过程归纳出五类持续存在的实践悖论：效率与公平、数据利用与隐私、自动化与人本沟通、技术更新与组织承载、量化判断与具体情境。文章认为，人工智能在人力资源管理中的价值取决于制度安排和使用方式，而非模型性能本身；对高影响人事决策实行分级管理、保留有意义的人工审查、建立解释和申诉渠道，是推进负责任应用的基本条件。

关键词

关键词：人工智能；人力资源管理；智能人力资源管理；算法治理；人机协同；研究综述

Abstract

Abstract: AI is no longer used in human resource management only to automate clerical work. Organizations also apply it to screening applicants, recommending training, drafting performance feedback, providing compensation and employee services, and planning the workforce. The literature reports faster information handling and stronger predictive support, but these improvements do not in themselves secure procedural fairness, employee trust, or better organizational results. This article reviews Chinese and international studies published between 2015 and 2026. Rather than counting topics mechanically, it compares how the field has defined AI-enabled HRM, how its questions have changed, which theories have been used, and what disputes have emerged in practice. The review finds a movement from digitizing processes, to prediction and recommendation, and then to governance of values and responsibility. Chinese research has concentrated on implementation and organizational transformation; international research addressed discrimination, labor control, rights, and accountability earlier. A five-level framework links technological conditions, organizational arrangements, individual responses, value constraints, and management outcomes. Five recurring tensions are then examined: efficiency and fairness, data use and privacy, automation and human communication, rapid technical change and organizational capacity, and quantitative indicators and contextual judgment. The article concludes that model performance alone cannot determine whether AI-enabled HRM is responsible. What matters is how outputs enter decisions, who may question them, and who remains accountable. High-impact decisions should therefore be subject to differentiated risk controls, genuine human review, understandable reasons, appeal routes, employee participation, and auditable responsibility.

Keywords

Keywords: artificial intelligence; human resource management; intelligent human resource management; algorithmic governance; human–AI collaboration; literature review

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引用本文

罗子敬. 人工智能赋能人力资源管理研究综述：理论框架、实践悖论与研究缺口[J]. 人文与社会科学学刊. 2026, 2 (8): 13-21. DOI: 10.70693/202607121395.

APA引用

罗子敬. (2026). 人工智能赋能人力资源管理研究综述：理论框架、实践悖论与研究缺口. 人文与社会科学学刊, 2 (8), 13-21. https://doi.org/10.70693/202607121395

参考文献

参考文献：
[1] 罗文豪, 霍伟伟, 赵宜萱, 等. 人工智能驱动的组织与人力资源管理变革: 实践洞察与研究方向[J]. 中国人力资源开发, 2022, 39(1): 4-16.
[2] TAMBE P, CAPPELLI P, YAKUBOVICH V. Artificial intelligence in human resources management: challenges and a path forward[J]. California Management Review, 2019, 61(4): 15-42.
[3] PAN Y, FROESE F J. An interdisciplinary review of AI and HRM: challenges and future directions[J]. Human Resource Management Review, 2023, 33(1): 100924.
[4] VRONTIS D, CHRISTOFI M, PEREIRA V, et al. Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review[J]. The International Journal of Human Resource Management, 2022, 33(6): 1237-1266.
[5] STONE D L, DEADRICK D L, LUKASZEWSKI K M, et al. The influence of technology on the future of human resource management[J]. Human Resource Management Review, 2015, 25(2): 216-231.
[6] 张琪, 林佳怡, 陈璐, 等. 人工智能技术驱动下的人力资源管理: 理论研究与实践应用[J]. 电子科技大学学报(社会科学版), 2023, 25(1): 77-84.
[7] BUDHWAR P, MALIK A, DE SILVA M T T, et al. Artificial intelligence—challenges and opportunities for international HRM: a review and research agenda[J]. The International Journal of Human Resource Management, 2022, 33(6): 1065-1097.
[8] KELLOGG K C, VALENTINE M A, CHRISTIN A. Algorithms at work: the new contested terrain of control[J]. Academy of Management Annals, 2020, 14(1): 366-410.
[9] LEICHT-DEOBALD U, BUSCH T, SCHANK C, et al. The challenges of algorithm-based HR decision-making for personal integrity[J]. Journal of Business Ethics, 2019, 160(2): 377-392.
[10] AJUNWA I. The black box at work[J]. Big Data & Society, 2020, 7(2): 2053951720966181.
[11] BAROCAS S, SELBST A D. Big data’s disparate impact[J]. California Law Review, 2016, 104(3): 671-732.
[12] RAGHAVAN M, BAROCAS S, KLEINBERG J, et al. Mitigating bias in algorithmic hiring: evaluating claims and practices[C]//Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. New York: Association for Computing Machinery, 2020: 469-481.
[13] DAVIS F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS Quarterly, 1989, 13(3): 319-340.
[14] TORNATZKY L G, FLEISCHER M, CHAKRABARTI A K. The processes of technological innovation[M]. Lexington: Lexington Books, 1990.
[15] 谢小云, 左玉涵, 胡琼晶. 数字化时代的人力资源管理: 基于人与技术交互的视角[J]. 管理世界, 2021, 37(1): 200-216.
[16] RAISCH S, KRAKOWSKI S. Artificial intelligence and management: the automation-augmentation paradox[J]. Academy of Management Review, 2021, 46(1): 192-210.
[17] BARREDO ARRIETA A, DÍAZ-RODRÍGUEZ N, DEL SER J, et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI[J]. Information Fusion, 2020, 58: 82-115.
[18] NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY. Artificial Intelligence Risk Management Framework (AI RMF 1.0)[R/OL]. Gaithersburg: NIST, 2023[2026-07-08]. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf.
[19] OECD. OECD AI Principles[EB/OL]. Paris: OECD, 2019[2026-07-08]. https://oecd.ai/en/ai-principles.
[20] 全国人民代表大会常务委员会. 中华人民共和国个人信息保护法[EB/OL]. (2021-08-20)[2026-07-08]. https://www.gov.cn/xinwen/2021-08/20/content_5632486.htm.
[21] BOGEN M, RIEKE A. Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias[R/OL]. Washington, DC: Upturn, 2018[2026-07-08]. https://www.upturn.org/work/help-wanted/.
[22] 赵宜萱, 赵曙明, 栾佳锐. 基于人工智能的人力资源管理: 理论模型及研究展望[J]. 南京社会科学, 2020(2): 36-43.
[23] 李燕萍, 李乐, 胡翔. 数字化人力资源管理: 整合框架与研究展望[J]. 科技进步与对策, 2021, 38(23): 151-160.
[24] 张敏, 赵宜萱. 机器学习在人力资源管理领域中的应用研究[J]. 中国人力资源开发, 2022, 39(1): 71-83.
[25] 陈鼎祥, 刘帮成. 人工智能时代的公共部门人力资源管理: 实践应用与理论研究[J]. 公共管理与政策评论, 2022, 11(4): 38-51.
[26] 张亚莉, 李辽辽, 丁振斌. 组织管理中的人工智能决策: 述评与展望[J]. 外国经济与管理, 2024, 46(10): 18-38.
[27] 喻术红, 常春. 算法劳动管理的伦理诉求及其法制建设研究[J]. 华南理工大学学报(社会科学版), 2022, 24(5): 52-59.
[28] 蒋路远, 曹李梅, 秦昕, 等. 人工智能决策的公平感知[J]. 心理科学进展, 2022, 30(5): 1078-1092.
[29] 刘善仕, 裴嘉良, 葛淳棉, 等. 在线劳动平台算法管理: 理论探索与研究展望[J]. 管理世界, 2022, 38(2): 225-239.
[30] 彭剑锋. 新一代人工智能对组织与人力资源管理的影响与挑战[J]. 中国人力资源开发, 2023, 40(7): 8-14.
[31] 贺伟, 汪林, 吴小玥. 人工智能技术对人力资源管理研究的影响述评[J]. 中国科学基金, 2024, 38(5): 831-840.
[32] 胡心约, 张恬路, 李英武. 基于AI的情绪识别在组织中的实践: 现状、未来和挑战[J]. 中国人力资源开发, 2022, 39(1): 57-70.
[33] 付春香, 闫宇航. 算法与认知：人工智能驱动的人力资源管理创新[J]. 科技创业月刊, 2024, 37(8): 22-27.
[34] 高记, 冯婧雯. 不利结果下算法决策对员工公平感的双刃剑效应: 基于归因理论的视角[J]. 中国人力资源开发, 2024, 41(5): 36-53.
[35] 包艳, 马伟博, 廖建桥, 等. 算法权力在管理领域的研究回顾、探索与展望[J]. 外国经济与管理, 2024, 46(5): 120-135.