人工智能技术的迅速发展为企业管理带来了重要机遇,也提出了新的治理挑战。企业人工智能价值对齐作为降低价值偏离、伦理风险和安全风险的重要治理路径,受到越来越多的关注。相较而言,国内相关研究仍处于起步阶段。鉴于此,本研究对现有文献进行系统性梳理和归纳,阐明人工智能价值对齐的概念内涵、结构维度及其测量方式,构建人工智能价值对齐的实现机制,并对企业人工智能价值对齐的前因和效应进行梳理,形成整合性的理解框架,最后对该领域的未来研究方向进行展望。本研究有助于促进对人工智能价值对齐的深层次理解,并为国内企业人工智能价值对齐的实证研究和实践应用提供参考。
企业人工智能价值对齐:研究进展与未来展望
摘要
参考文献
1 付业辉, 僧建芬, 乐凯迪, 等. 人工智能应用与企业价值链升级: 效应、机制与情境异质性——大语言模型的文本分析证据[J]. 科技进步与对策, 2026, 43(8): 26-36.
5 李海舰, 赵丽. 数据价值理论研究[J]. 财贸经济, 2023, 44(6): 5-20.
6 李思雯. 人工智能价值对齐的路径探析[J]. 伦理学研究, 2024, (5): 99-108. DOI:10.3969/j.issn.1671-9115.2024.05.012
7 凌斌, 贺筱颖, 王晓辰. AI算法视域下决策建议来源对验证性信息偏差的影响[J]. 心理科学, 2025, 48(5): 1185-1196. DOI:10.16719/j.cnki.1671-6981.20250514
8 裴嘉良, 刘善仕, 钟楚燕, 等. AI算法决策能提高员工的程序公平感知吗?[J]. 外国经济与管理, 2021, 43(11): 41-55.
9 宋保林. 人工智能大模型价值对齐的伦理建构[J]. 伦理学研究, 2025, (3): 94-99. DOI:10.3969/j.issn.1671-9115.2025.03.012
11 唐林垚. 公司法如何促进模型可信与价值对齐[J]. 东方法学, 2024, (2): 76-87. DOI:10.3969/j.issn.1007-1466.2024.02.008
12 王戈, 张哲君. 任务客观性、情感相似度何以影响算法决策感知公平与接受度?——基于调查实验的实证分析[J]. 公共管理与政策评论, 2023, 12(6): 77-95. DOI:10.3969/j.issn.2095-4026.2023.06.007
15 夏永红. 人工智能伦理治理范式: 从价值对齐到价值共生[J]. 自然辩证法通讯, 2025, 47(1): 1-8. DOI:10.15994/j.1000-0763.2025.01.001
16 闫坤如. 人工智能体价值对齐的分布式路径探赜[J]. 上海师范大学学报(哲学社会科学版), 2024, 53(4): 131-139. DOI:10.13852/J.CNKI.JSHNU.2024.04.013
18 矣晓沅, 谢幸. 大模型道德价值观对齐问题剖析[J]. 计算机研究与发展, 2023, 60(9): 1926-1945. DOI:10.7544/issn1000-1239.202330553
19 易显飞, 高津宇. 幻觉的蒸馏: 生成式人工智能价值观三条对齐路径的价值风险解析[J]. 东岳论丛, 2026, 47(1): 26-34,191. DOI:10.3969/j.issn.1003-8353.2026.01.003
20 曾雯, 侯凌风, 周旅军. 价值对齐: 人工智能时代的技术伦理与文明对话[J]. 贵州民族大学学报(哲学社会科学版), 2025, (6): 172-191. DOI:10.3969/j.issn.1003-6644.2025.06.011
21 赵艺. “人工智能+”时代的价值对齐: 困境与治理[J/OL]. 科学学研究, 2026: 1-14.
22 赵一骏, 许丽颖, 喻丰, 等. 感知不透明性增加职场中的算法厌恶[J]. 心理学报, 2024, 56(4): 497-514. DOI:10.3724/SP.J.1041.2024.00497
23 周恋, 雷雪, 后锐, 等. 在线用工平台算法管理的消极影响和控制策略研究: 算法技术属性视角[J]. 中国人力资源开发, 2022, 39(6): 8-22.
24 Abadi M, Chu A, Goodfellow I, et al. Deep learning with differential privacy[A]. Proceedings of 2016 ACM SIGSAC conference on computer and communications security[C]. Vienna: ACM, 2016.
25 Abositta A, Adedokun M W, Berberoğlu A. Influence of artificial intelligence on engineering management decision-making with mediating role of transformational leadership[J]. Systems, 2024, 12(12): 570. DOI:10.3390/systems12120570
26 Acikgoz Y, Davison K H, Compagnone M, et al. Justice perceptions of artificial intelligence in selection[J]. International Journal of Selection and Assessment, 2020, 28(4): 399-416. DOI:10.1111/ijsa.12306
27 Ali B A. AI integrated approach to achieve transformational strategic leadership and its reflections on employee engagement[A]. The International Conference on Artificial Intelligence Management and Trends[C], 2025, 87.
28 Baker J, Cao Q, Jones D, et al. Dynamic strategic alignment competency: A theoretical framework and an operationalization[R]. Working Paper, 2009.
Baker J, Cao Q, Jones D, et al. Dynamic strategic alignment competency: A theoretical framework and an operationalization[R]. Working Paper, 2009.
29 Barabadi E, Fotuhabadi Z, Arghavan A, et al. Comparing AI and human moral reasoning: Context-sensitive patterns beyond utilitarian bias[J]. Frontiers in Artificial Intelligence, 2026, 8: 1710410. DOI:10.3389/frai.2025.1710410
30 Beheshti A, Kerridge I. Understanding the artificial intelligence revolution and its ethical implications[J]. Journal of Bioethical Inquiry, 2025, 22(3): 497-505. DOI:10.1007/s11673-025-10427-6
31 Boncella R. AI and management: Navigating the alignment problem for ethical and effective decision-making[J]. Issues in Information Systems, 2024, 25(4): 194-204.
32 Bostrom N. Superintelligence: Paths, dangers, strategies[M]. Oxford: Oxford University Press, 2014.
33 Braun M, Greve M, Gnewuch U.The new dream team? A review of human-AI collaboration research from a human teamwork perspective[A]. Proceedings of the International Conference on Information Systems[C]. Hyderabad: Association for Information Systems, 2023.
34 Brown C V. Examining the emergence of hybrid IS governance solutions: Evidence from a single case site[J]. Information Systems Research, 1997, 8(1): 69-94. DOI:10.1287/isre.8.1.69
35 Brown O, Davison R M, Decker S, et al. Theory-driven perspectives on generative artificial intelligence in business and management[J]. British Journal of Management, 2024, 35(1): 3-23. DOI:10.1111/1467-8551.12788
36 Cai Y N, Wang C. Effect of transparency of algorithmic performance management on proactive work behavior: Role of motivation to improve performance and psychological ownership[J]. International Journal of Asian Business and Information Management (IJABIM), 2024, 15(1): 1-17.
37 Capasso M, Arora P, Sharma D, et al. On the right to work in the age of artificial intelligence: Ethical safeguards in algorithmic human resource management[J]. Business and Human Rights Journal, 2024, 9(3): 346-360. DOI:10.1017/bhj.2024.26
38 Carter D. Regulation and ethics in artificial intelligence and machine learning technologies: Where are we now? Who is responsible? Can the information professional play a role?[J]. Business Information Review, 2020, 37(2): 60-68. DOI:10.1177/0266382120923962
39 Cath C. Governing artificial intelligence: Ethical, legal and technical opportunities and challenges[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2018, 376(2133): 20180080. DOI:10.1098/rsta.2018.0080
40 Chan Y E, Reich B H. IT alignment: What have we learned?[J]. Journal of Information Technology, 2007, 22(4): 297-315. DOI:10.1057/palgrave.jit.2000109
41 Cheong B C. Transparency and accountability in AI systems: Safeguarding wellbeing in the age of algorithmic decision-making[J]. Frontiers in Human Dynamics, 2024, 6: 1421273. DOI:10.3389/fhumd.2024.1421273
42 Chung Y W,Im S,Kim J E,et al. Artificial intelligence awareness, career resilience, job insecurity and behavioural outcomes[J]. Australian Journal of Psychology, 2025, 77(1): 2559910. DOI:10.1080/00049530.2025.2559910
43 Csaszar F A, Ketkar H, Kim H. Artificial intelligence and strategic decision-making: Evidence from entrepreneurs and investors[J]. Strategy Science, 2024, 9(4): 322-345. DOI:10.1287/stsc.2024.0190
44 de Laat P B. Companies committed to responsible AI: From principles towards implementation and regulation?[J]. Philosophy & Technology, 2021, 34(4): 1135-1193.
45 Decker M C, Wegner L, Leicht-Scholten C. Procedural fairness in algorithmic decision-making: The role of public engagement[J]. Ethics and Information Technology, 2025, 27(1): 1. DOI:10.1007/s10676-024-09811-4
46 Ebers M. Truly risk-based regulation of artificial intelligence: How to implement the EU’s AI Act[J]. European Journal of Risk Regulation, 2025, 16(2): 684-703. DOI:10.1017/err.2024.78
47 Enholm I M, Papagiannidis E, Mikalef P, et al. Artificial intelligence and business value: A literature review[J]. Information Systems Frontiers, 2022, 24(5): 1709-1734. DOI:10.1007/s10796-021-10186-w
48 Erdélyi O J, Goldsmith J. Regulating artificial intelligence: Proposal for a global solution[A]. Proceedings of the 2018 AAAI/ACM conference on AI, ethics, and society[C]. New Orleans: ACM, 2018.
49 Fabris A, Baranowska N, Dennis M J, et al. Fairness and bias in algorithmic hiring: A multidisciplinary survey[J]. ACM Transactions on Intelligent Systems and Technology, 2025, 16(1): 16.
50 Feldkamp T, Langer M, Wies L, et al. Justice, trust, and moral judgements when personnel selection is supported by algorithms[J]. European Journal of Work and Organizational Psychology, 2024, 33(2): 130-145. DOI:10.1080/1359432X.2023.2169140
51 Ferrara E. Addressing racial bias in AI: Towards a more equitable future[A]. Czarnowski I, Howlett R J, Jain L C. Intelligent decision technologies[M]. Singapore: Springer, 2025.
52 Firt E. Addressing corrigibility in near-future AI systems[J]. AI and Ethics, 2025, 5(2): 1481-1490. DOI:10.1007/s43681-024-00484-9
53 Floridi L, Cowls J. A unified framework of five principles for AI in society[A]. Carta S. Machine learning and the city: Applications in architecture and urban design[M]. Hoboken: John Wiley & Sons Ltd, 2022.
54 Floridi L, Cowls J, King T C, et al. How to design AI for social good: Seven essential factors[A]. Floridi L. Ethics, governance, and policies in artificial intelligence[M]. Cham: Springer, 2021.
55 Gabriel I. Artificial intelligence, values, and alignment[J]. Minds and Machines, 2020, 30(3): 411-437. DOI:10.1007/s11023-020-09539-2
56 Gabriel I, Ghazavi V. The challenge of value alignment: From fairer algorithms to AI safety[A]. Véliz C. The Oxford handbook of digital ethics[M]. Oxford: Oxford University Press, 2022.
57 García-Madurga M Á, Gil-Lacruz A I, Saz-Gil I, et al. The role of artificial intelligence in improving workplace well-being: A systematic review[J]. Businesses, 2024, 4(3): 389-410. DOI:10.3390/businesses4030024
58 Gehman S, Gururangan S, Sap M, et al. RealToxicityPrompts: Evaluating neural toxic degeneration in language models[A]. Findings of the Association for Computational Linguistics: EMNLP 2020[C]. 2020: 3356-3369.
59 Gkinko L, Elbanna A. Designing trust: The formation of employees’ trust in conversational AI in the digital workplace[J]. Journal of Business Research, 2023, 158: 113707. DOI:10.1016/j.jbusres.2023.113707
60 Glikson E, Woolley A W. Human trust in artificial intelligence: Review of empirical research[J]. Academy of Management Annals, 2020, 14(2): 627-660. DOI:10.5465/annals.2018.0057
61 Habbal A, Ali M K, Abuzaraida M A. Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions[J]. Expert Systems with Applications, 2024, 240: 122442. DOI:10.1016/j.eswa.2023.122442
62 Hadfield-Menell D, Dragan A, Abbeel P, et al. Cooperative inverse reinforcement learning[A]. Proceedings of the 30th international conference on neural information processing systems[C]. Barcelona: Curran Associates Inc., 2016.
63 Hickman E, Petrin M. Trustworthy AI and corporate governance: The EU’s ethics guidelines for trustworthy artificial intelligence from a company law perspective[J]. European Business Organization Law Review, 2021, 22(4): 593-625. DOI:10.1007/s40804-021-00224-0
64 Hiremath S, P R, Konek S S, et al. Artificial intelligence (AI) governance in organizational decision-making: Balancing autonomy, accountability and transparency[J]. Journal of Entrepreneurship and Public Policy, 2025: 1-24.
65 Hristova T, Magee L, Soldatic K. The problem of alignment[J]. AI & Society, 2025, 40(3): 1439-1453.
66 Janssen M, Brous P, Estevez E, et al. Data governance: Organizing data for trustworthy artificial intelligence[J]. Government Information Quarterly, 2020, 37(3): 101493. DOI:10.1016/j.giq.2020.101493
67 Jaung W. Does AI value the environment? Evaluation of AI value alignment[J]. Technological Forecasting and Social Change, 2026, 225: 124550. DOI:10.1016/j.techfore.2026.124550
68 Ji J M, Qiu T Y, Chen B Y, et al. AI alignment: A comprehensive survey[Z]. arXiv: 2310.19852, 2023.
69 Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines[J]. Nature Machine Intelligence, 2019, 1(9): 389-399. DOI:10.1038/s42256-019-0088-2
70 Josifović S. Legal and administrative frameworks as foundations for AI alignment with human volition[J]. AI and Ethics, 2025, 5(3): 3057-3067. DOI:10.1007/s43681-024-00640-1
71 Kim S. Perceptions of discriminatory decisions of artificial intelligence: Unpacking the role of individual characteristics[J]. International Journal of Human-Computer Studies, 2025, 194: 103387. DOI:10.1016/j.ijhcs.2024.103387
72 Kumar S, Datta S, Singh V, et al. Applications, challenges, and future directions of human-in-the-loop learning[J]. IEEE Access, 2024, 12: 75735-75760. DOI:10.1109/ACCESS.2024.3401547
73 Leike J, Krueger D, Everitt T, et al. Scalable agent alignment via reward modeling: A research direction[Z]. arXiv: 1811.07871, 2018.
74 Leslie D. Understanding artificial intelligence ethics and safety[Z]. arXiv: 1906.05684, 2019.
75 Liu Z, Lin Q X, Tu S M, et al. When robot knocks, knowledge locks: How and when does AI awareness affect employee knowledge hiding?[J]. Frontiers in Psychology, 2025, 16: 1627999. DOI:10.3389/fpsyg.2025.1627999
76 Lu Y. Artificial intelligence: A survey on evolution, models, applications and future trends[J]. Journal of Management Analytics, 2019, 6(1): 1-29. DOI:10.1080/23270012.2019.1570365
77 Madanchian M, Taherdoost H. Ethical theories, governance models, and strategic frameworks for responsible AI adoption and organizational success[J]. Frontiers in Artificial Intelligence, 2025, 8: 1619029. DOI:10.3389/frai.2025.1619029
78 Majrashi K. Employees’ perceptions of the fairness of AI-based performance prediction features[J]. Cogent Business & Management, 2025, 12(1): 2456111.
79 Marklund H, Infanger A, Van Roy B. Misalignment from treating means as ends[Z]. arXiv: 2507.10995, 2025.
80 Martin K. Ethical implications and accountability of algorithms[J]. Journal of Business Ethics, 2019, 160(4): 835-850. DOI:10.1007/s10551-018-3921-3
81 Matthews M J, Su R K, Yonish L, et al. A review of artificial intelligence, algorithms, and robots through the lens of stakeholder theory[J]. Journal of Management, 2025, 51(6): 2627-2676. DOI:10.1177/01492063241311855
82 Meng Q, Wu T J, Duan W, et al. Effects of employee–artificial intelligence (AI) collaboration on counterproductive work behaviors (CWBs): Leader emotional support as a moderator[J]. Behavioral Sciences, 2025, 15(5): 696. DOI:10.3390/bs15050696
83 Mkhize S, Lourens M. Elevating knowledge sharing and communication through artificial intelligence: An organizational perspective[J]. International Journal of Research in Business and Social Science, 2025, 14(4): 103-114.
84 Mohammadi H, Bagheri A. Exploring cultural variations in moral judgments with large language models[Z]. arXiv: 2506.12433, 2025.
85 Mökander J, Floridi L. Ethics-based auditing to develop trustworthy AI[J]. Minds and Machines, 2021, 31(2): 323-327. DOI:10.1007/s11023-021-09557-8
86 Norhashim H, Hahn J. Measuring human-AI value alignment in large language models[A]. Proceedings of the 7th AAAI/ACM conference on AI, ethics, and society[C]. San Jose: AAAI, 2024.
87 Nouis S C, Uren V, Jariwala S. Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: A qualitative study of healthcare professionals’ perspectives in the UK[J]. BMC Medical Ethics, 2025, 26(1): 89. DOI:10.1186/s12910-025-01243-z
88 Novelli C, Taddeo M, Floridi L. Accountability in artificial intelligence: What it is and how it works[J]. AI & Society, 2024, 39(4): 1871-1882.
89 Obermeyer Z, Powers B, Vogeli C, et al. Dissecting racial bias in an algorithm used to manage the health of populations[J]. Science, 2019, 366(6464): 447-453. DOI:10.1126/science.aax2342
90 Ochmann J, Michels L, Tiefenbeck V, et al. Perceived algorithmic fairness: An empirical study of transparency and anthropomorphism in algorithmic recruiting[J]. Information Systems Journal, 2024, 34(2): 384-414. DOI:10.1111/isj.12482
91 Omohundro S M. The basic AI drives[A]. Proceedings of the 1st AGI conference[C]. Memphis: IOS Press, 2008.
92 Ouyang L, Wu J, Jiang X, et al. Training language models to follow instructions with human feedback[A]. Proceedings of the 36th international conference on neural information processing systems[C]. New Orleans: Curran Associates Inc., 2022.
93 Papagiannidis E, Enholm I M, Dremel C, et al. Toward AI governance: Identifying best practices and potential barriers and outcomes[J]. Information Systems Frontiers, 2023, 25(1): 123-141. DOI:10.1007/s10796-022-10251-y
94 Park H J. Patient perspectives on informed consent for medical AI: A web-based experiment[J]. Digital Health, 2024, 10.
95 Park K, Yoon H Y. AI algorithm transparency, pipelines for trust not prisms: Mitigating general negative attitudes and enhancing trust toward AI[J]. Humanities and Social Sciences Communications, 2025, 12(1): 1160. DOI:10.1057/s41599-025-05116-z
96 Park S, Nan X L. Generative AI and misinformation: A scoping review of the role of generative AI in the generation, detection, mitigation, and impact of misinformation[J]. AI & Society, 2026, 41(2): 1501-1515.
97 Pelletier C, Raymond L. Investigating the strategic IT alignment process with a dynamic capabilities view: A multiple case study[J]. Information & Management, 2024, 61(4): 103819.
98 Raji I D, Smart A, White R N, et al. Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing[A]. Proceedings of 2020 conference on fairness, accountability, and transparency[C]. Barcelona: ACM, 2020.
99 Rane N, Choudhary S P, Rane J. Acceptance of artificial intelligence: Key factors, challenges, and implementation strategies[J]. Journal of Applied Artificial Intelligence, 2024, 5(2): 50-70. DOI:10.48185/jaai.v5i2.1017
100 Russell S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence[J]. AI Magazine, 2015, 36(4): 105-114. DOI:10.1609/aimag.v36i4.2577
101 Russell S J. Human compatible: Artificial intelligence and the problem of control[M]. London: Allen Lane, 2019.
102 Sabherwal R, Hirschheim R, Goles T. The dynamics of alignment: Insights from a punctuated equilibrium model[J]. Organization Science, 2001, 12(2): 179-197. DOI:10.1287/orsc.12.2.179.10113
103 Saeidnia H R, Jahani S, Ghiasi N, et al. Generative AI and health misinformation: Production, propagation, and mitigation—a systematic review[J]. BMC Public Health, 2026, 26(1): 693. DOI:10.1186/s12889-025-26148-9
104 Salaheldin S, Hussein S. The determinants of AI adoption and its impact on employee engagement: Evidence from Egyptian organizations[J]. International Journal of Management and Applied Research, 2025, 12(2): 45-67. DOI:10.18646/2056.122.25-004
105 Sarkar A, Faik I. Structural transparency of societal AI alignment through institutional logics[Z]. arXiv: 2602.08246, 2026.
106 Schilke O, Reimann M. The transparency dilemma: How AI disclosure erodes trust[J]. Organizational Behavior and Human Decision Processes, 2025, 188: 104405. DOI:10.1016/j.obhdp.2025.104405
107 Seghid N, Iqbal F, Al-Room K, et al. Emerging threats in AI: A detailed review of misuses and risks across modern AI technologies[J]. Frontiers in Communications and Networks, 2026, 6: 1727425. DOI:10.3389/frcmn.2025.1727425
108 Shah M, Sureja N. A comprehensive review of bias in deep learning models: Methods, impacts, and future directions[J]. Archives of Computational Methods in Engineering, 2025, 32(1): 255-267. DOI:10.1007/s11831-024-10134-2
109 Shen H, Knearem T, Ghosh R, et al. Towards bidirectional human-AI alignment: A systematic review for clarifications, framework, and future directions[Z]. arXiv: 2406.09264v1, 2024.
110 Sorensen T, Jiang L W, Hwang J D, et al. Value kaleidoscope: Engaging AI with pluralistic human values, rights, and duties[A]. Proceedings of the 38th AAAI conference on artificial intelligence[C]. Vancouver: AAAI, 2024.
111 Stahl B C, Antoniou J, Ryan M, et al. Organisational responses to the ethical issues of artificial intelligence[J]. AI & Society, 2022, 37(1): 23-37.
112 Stanikzai M E, Mittal E. Strategic alignment of organizational structure based on decisions for sustainable organizational performance: A bibliometric-systematic literature review[J]. Cogent Business & Management, 2025, 12(1): 2560650.
113 Tamkin A, Brundage M, Clark J, et al. Understanding the capabilities, limitations, and societal impact of large language models[Z]. arXiv: 2102.02503, 2021.
114 Tang P M, Koopman J, Mai K M, et al. No person is an island: Unpacking the work and after-work consequences of interacting with artificial intelligence[J]. Journal of Applied Psychology, 2023, 108(11): 1766-1789. DOI:10.1037/apl0001103
115 Tulis M, Dresel M. Effects on and consequences of responses to errors: Results from two experimental studies[J]. British Journal of Educational Psychology, 2025, 95(1): 143-161. DOI:10.1111/bjep.12686
116 Übellacker T. Making sense of AI limitations: How individual perceptions shape organizational readiness for AI adoption[Z]. arXiv: 2502.15870, 2025.
117 Van de Wetering R. Dynamic enterprise architecture capabilities and organizational benefits: An empirical mediation study[Z]. arXiv: 2105.10036, 2021.
118 Von Zahn M, Liebich L, Jussupow E, et al. Knowing (not) to know: Explainable artificial intelligence and human metacognition[J]. Information Systems Research, 2025, doi: 10.1287/isre.2024.1431.
119 Watson E, Viana T, Zhang S J, et al. Towards an end-to-end personal fine-tuning framework for AI value alignment[J]. Electronics, 2024, 13(20): 4044. DOI:10.3390/electronics13204044
120 Wiener N. Some moral and technical consequences of automation: As machines learn they may develop unforeseen strategies at rates that baffle their programmers[J]. Science, 1960, 131(3410): 1355-1358. DOI:10.1126/science.131.3410.1355
121 Xia H S, Chen H, Zhang J Z, et al. Exploring the impact of responsible AI governance on corporate performance: A quasi-natural experiment[J]. Technological Forecasting and Social Change, 2026, 223: 124425. DOI:10.1016/j.techfore.2025.124425
122 Yang J F, Xie W Y, Ni J J. A framework for AI ethics literacy: Development, validation, and its role in fostering students’ self-rated learning competence[J]. Scientific Reports, 2025, 15(1): 38030. DOI:10.1038/s41598-025-21977-5
123 Young M M, Bullock J B, Lecy J D. Artificial discretion as a tool of governance: A framework for understanding the impact of artificial intelligence on public administration[J]. Perspectives on Public Management and Governance, 2019, 2(4): 301-313.
124 Yu L R, Li Y. Artificial intelligence decision-making transparency and employees’ trust: The parallel multiple mediating effect of effectiveness and discomfort[J]. Behavioral Sciences, 2022, 12(5): 127. DOI:10.3390/bs12050127
125 Yu L R, Li Y, Fan F. Employees’ appraisals and trust of artificial intelligences’ transparency and opacity[J]. Behavioral Sciences, 2023, 13(4): 344. DOI:10.3390/bs13040344
126 Yuan Y, Shi Y, Su T, et al. Resistance or compliance? The impact of algorithmic awareness on people’s attitudes toward online information browsing[J]. Frontiers in Psychology, 2025, 16: 1563592. DOI:10.3389/fpsyg.2025.1563592
127 Yudkowsky E. Artificial intelligence as a positive and negative factor in global risk[A]. Bostrom N, Ćirković M M. Global catastrophic risks[M]. Oxford: Oxford University Press, 2008.
128 Zárate-Torres R, Rey-Sarmiento C F, Acosta-Prado J C, et al. Influence of leadership on human-artificial intelligence collaboration[J]. Behavioral Sciences, 2025, 15(7): 873. DOI:10.3390/bs15070873
引用本文
王雁飞, 张晓念, 朱瑜. 企业人工智能价值对齐:研究进展与未来展望[J]. 外国经济与管理, 2026, 48(8): 77-94.
导出参考文献,格式为:





206
146
