AI时代的前瞻性数据治理：数据访问、复用与主权中的新兴信号
Anticipatory Data Governance in the Age of AI: Emerging Signals in Data Access, Reuse, and Sovereignty
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中文总结 AI 辅助
本研究通过专家预测工作坊识别出AI时代数据治理的7个新兴信号及强化反馈循环，提出与多领域融合的前瞻性数据治理议程，为结构性转变提供诊断框架。
中文摘要 AI 辅助
本文报告了由The GovLab在2025至2026年间举办的两场专家预测工作坊所开展的结构化参与式前瞻研究的发现。这些工作坊汇聚了来自多个司法管辖区的19名资深从业者，涵盖官方统计、数字与贸易政策、开放科学、AI治理、地理空间系统及公共部门创新领域。我们采用基于地平线扫描与前瞻性治理传统的定性信号扫描方法，梳理、聚类并主题综合了数据访问、治理与复用领域的新兴发展，并结合从业者经验对其进行压力测试。我们识别出7个趋同信号：1）开放数据范式正承受压力；2）数据生态系统正变得以机器为中心且由AI介导；3）推理正在重塑数据治理的基础；4）数据基础设施正变得更难维持；5）治理在各机构与司法管辖区间碎片化；6）主权与安全正推动转向战略控制；7）数据共享模式需要更强的激励与利益共享机制。我们进一步绘制了耦合这些信号的强化反馈循环，展示了某一领域的干预如何在更广泛的生态系统中传播风险与机遇。我们认为，数据治理正与AI治理、数字公共基础设施、经济战略、民主韧性及地缘政治竞争不可分割，我们还概述了前瞻性数据治理的议程，该议程能在依赖关系、风险与错失的机会被锁定前进行调整。本研究的贡献是诊断性而非预测性的：这些信号提供了一个基于证据的框架，用于思考已在进行的结构性转变，而非对特定技术结果的预测。
英文摘要
This paper reports findings from a structured participatory foresight study comprising two expert forecasting studios convened by The GovLab between 2025 and 2026. The studios brought together nineteen senior practitioners spanning official statistics, digital and trade policy, open science, AI governance, geospatial systems, and public-sector innovation across multiple jurisdictions. Applying a qualitative signal-scanning methodology grounded in the horizon-scanning and anticipatory-governance traditions, we elicited, clustered, and thematically synthesized emerging developments in data access, governance,and reuse, and stress-tested them against practitioner experience. We identify seven convergent signals: (1) the open-data paradigm is under strain; (2) data ecosystems are becoming machine-centric and AI-mediated; (3) inference is reshaping the foundations of data governance;(4) data infrastructure is becoming harder to sustain; (5) governance is fragmenting across institutions and jurisdictions; (6) sovereignty and security are driving a turn toward strategic control; and (7) data-sharing models require stronger incentives and benefit-sharing mechanisms. We further map the reinforcing feedback loops that couple these signals, showing how interventions in one domain propagate risks and opportunities across the wider ecosystem. We argue that data governance is becoming inseparable from AI governance, digital public infrastructure, economic strategy, democratic resilience, and geopolitical competition, and we outline an agenda for anticipatory data governance capable of adapting before dependencies, risks, and missed opportunities become locked in. The contribution is diagnostic rather than predictive: the signals offer an evidence-informed framework for reasoning about structural shifts already underway, not a forecast of specific technological outcomes.
