کژساخته‌‌های هوش مصنوعی در سیاستگذاری و توسعه فناوری: از خطای تحلیلی تا خطای تصمیم

سخن سردبیر

نویسندگان

1 استادیارآینده پژوهی، مؤسسه تحقیقات سیاست علمی کشور، تهران، ایران

2 استادیارمدیریت فناوری، مؤسسه تحقیقات سیاست علمی کشور، تهران، ایران

چکیده

کژساخته‌‌های هوش مصنوعی در سیاستگذاری و توسعه فناوری: از خطای تحلیلی تا خطای تصمیم

کلیدواژه‌ها

عنوان مقاله [English]

Artificial Intelligence Artifacts in Policymaking and Technology Development: From Analytical Error to Decision Error

نویسندگان [English]

  • Reza hafezi 1
  • Farkhondeh Malekifar 2
1 Assistant Professor of Futures StudiesNational Research Institute for Science Policy, Tehran, Iran
2 Assistant Professor of Technology Management,,, National Research Institute for Science Policy, Tehran, Iran
چکیده [English]

Artificial Intelligence Artifacts in Policymaking and Technology Development: From Analytical Error to Decision Error

کلیدواژه‌ها [English]

  • Artificial intelligence
  • distorted
  • technology policymaking
  • decision error
  • foresight

مراجع

Barrett, J. F., & Keat, N. (2004). Artifacts in CT: Recognition and avoidance. Radiographics, 24(6), 1679-1691. https://doi.org/10.1148/rg.246045065
Borup, M., Brown, N., Konrad, K., & Van Lente, H. (2006). The sociology of expectations in science and technology. Technology Analysis & Strategic Management, 18(3-4), 285-298. https://doi.org/10.1080/09537320600777002
Cheong, B. C. (2024). Transparency and accountability in AI systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6, 1421273. https://doi.org/10.3389/fhumd.2024.1421273
de Bruijn, H., Warnier, M., & Janssen, M. (2022). The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making. Government Information Quarterly, 39(2), 101666. https://doi.org/10.1016/j.giq.2021.101666
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., ... & Ahmed, N. K. (2024). Bias and fairness in large language models: A survey. Computational Linguistics, 50(3), 1097-1179. https://doi.org/10.1162/coli_a_00524
Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case study. Research Policy, 31(8), 1257-1274. https://doi.org/10.1016/S0048-7333(02)00062-8
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121-127. https://doi.org/10.1136/amiajnl-2011-000089
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., ... & Liu, T. (2025). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems, 43(2), 1-55. https://doi.org/10.1145/3703155
Ibitoye, A. O., Nkwo, M. S., & Orji, R. (2025). Rethinking responsible AI from ethical pillars to sociotechnical practice. AI and Ethics, 5(6), 6207-6223. https://doi.org/10.1007/s43681-025-00809-2
Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2022). Will algorithms blind people? The effect of explainable AI and decision-makers’ experience on AI-supported decision-making in government. Social Science Computer Review, 40(2), 478-493.  https://doi.org/10.1177/0894439320980118
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
Leonardi, P. M., & Leavell, V. (2026). Knowing enough to be dangerous: The problem of “artificial certainty” for expert authority when using AI for decision making and planning. Organization Science, 37(2), 516-543. https://doi.org/10.1287/orsc.2023.18224
Lnenicka, M., Clarinval, A., Nikiforova, A., Rudmark, D., Luterek, M., Symeonidis, D., & Bolívar, M. P. R. (2026). Artificial intelligence in policymaking: Mapping integration gaps across the public policy cycle. Government Information Quarterly, 43(2), 102138. https://doi.org/10.1016/J.GIQ.2026.102138
Markard, J., & Truffer, B. (2008). Technological innovation systems and the multi-level perspective: Towards an integrated framework. Research Policy, 37(4), 596-615. https://doi.org/10.1016/j.respol.2008.01.004
Mikalef, P., Benlian, A., Conboy, K., & Tarafdar, M. (2025). Responsible AI starts with the artifact: Challenging the concept of responsible AI in IS research. European Journal of Information Systems, 34(3), 407-414. https://doi.org/10.1080/0960085X.2025.2506875
Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022). Thinking responsibly about responsible AI and ‘the dark side’ of AI. European Journal of Information Systems, 31(3), 257-268. https://doi.org/10.1080/0960085X.2022.2026621
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885
Resnik, D. B., & Hosseini, M. (2026). Hallucinated citations produced by generative artificial intelligence may constitute research misconduct when citations function as data in scholarly papers. Accountability in Research, 2645390. https://doi.org/10.1080/08989621.2026.2645390
Selten, F., Robeer, M., & Grimmelikhuijsen, S. (2023). ‘Just like I thought’: Street‐level bureaucrats trust AI recommendations if they confirm their professional judgment. Public Administration Review, 83(2), 263-278. https://doi.org/10.1111/puar.13602
Sharman, A., & Holmes, J. (2010). Evidence‐based policy or policy‐based evidence gathering? Biofuels, the EU and the 10% target. Environmental Policy and Governance, 20(5), 309-321. https://doi.org/10.1002/eet.543
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83. https://doi.org/10.1177/0008125619862257
Stoeber, T., Hammerschmidt, J., Lundervold, A., Kromidha, E., Kanbach, D. K., & Kraus, S. (2026). AI strategy under institutional pressure: strategic conformity and decision-making in large language models. Journal of Business Research, 212, 116227. https://doi.org/10.1016/j.jbusres.2026.116227
Zaitsev, M., Maclaren, J., & Herbst, M. (2015). Motion artifacts in MRI: A complex problem with many partial solutions. Journal of Magnetic Resonance Imaging, 42(4), 887-901. https://doi.org/10.1002/jmri.24850
 
 
 

فایل ها

هم رسانی

ارجاع به این مقاله

آمار