The future of artificial intelligence: Insights from recent Delphi studies

IF 3 3区 管理学 Q1 ECONOMICS
Ido Alon , Hazar Haidar , Ali Haidar , José Guimón
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引用次数: 0

Abstract

We review thirteen Delphi studies on the future of Artificial Intelligence (AI), published between 2014 and 2024. Using the Delphi method, an iterative approach that refines expert insights through multiple rounds, these studies provide foresight into AI’s technological advancements, societal impacts, and policy implications across various sectors. For example, Delphi studies in healthcare foresee significant advancements in AI-driven diagnostics and personalized medicine, while in manufacturing, AI is anticipated to enhance human-robot collaboration and supply chain optimization. AI’s impact on journalism and photography shows promise in automating processes and enriching immersive storytelling, although issues like data privacy and algorithmic bias are raised. This review emphasizes a primary focus on technology trajectories, examining anticipated developments and timelines, while also considering broader strategic foresight aspects. General challenges identified include equitable access, the need for robust data governance, and workforce upskilling to integrate AI responsibly. By synthesizing insights across these studies, we provide a structured overview of both opportunities and limitations in AI development, offering guidance for stakeholders to navigate AI's complexities and capitalize on its potential responsibly. In addition, we propose methodological recommendations, such as standardizing expert selection and diversifying perspectives to improve the quality of future Delphi studies.
人工智能的未来:来自最近德尔菲研究的见解
我们回顾了2014年至2024年间发表的13项关于人工智能(AI)未来的德尔菲研究。这些研究使用德尔菲法(一种通过多轮提炼专家见解的迭代方法),为人工智能的技术进步、社会影响和各个部门的政策影响提供了远见。例如,德尔福在医疗保健领域的研究预测,人工智能驱动的诊断和个性化医疗将取得重大进展,而在制造业,人工智能有望加强人机协作和供应链优化。人工智能对新闻和摄影的影响显示出在自动化流程和丰富沉浸式故事叙述方面的前景,尽管也提出了数据隐私和算法偏见等问题。这项审查强调主要关注技术轨迹,审查预期的发展和时间表,同时也考虑到更广泛的战略远见方面。确定的一般挑战包括公平获取、对强大数据治理的需求以及提高劳动力技能以负责任地整合人工智能。通过综合这些研究的见解,我们提供了人工智能发展的机遇和局限性的结构化概述,为利益相关者提供指导,以应对人工智能的复杂性并负责任地利用其潜力。此外,我们提出了方法学上的建议,如规范专家选择和多样化的视角,以提高未来德尔菲研究的质量。
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
Futures
Futures Multiple-
CiteScore
6.00
自引率
10.00%
发文量
124
期刊介绍: Futures is an international, refereed, multidisciplinary journal concerned with medium and long-term futures of cultures and societies, science and technology, economics and politics, environment and the planet and individuals and humanity. Covering methods and practices of futures studies, the journal seeks to examine possible and alternative futures of all human endeavours. Futures seeks to promote divergent and pluralistic visions, ideas and opinions about the future. The editors do not necessarily agree with the views expressed in the pages of Futures
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