GCRI · 人工智能与自主系统
依存关系与后果
AI 系统 depend on data, compute, energy, software supply chains and people, and they are increasingly embedded in services and infrastructures that others depend on. This page explains the causal, physical, economic, social and ecological links between AI and other systems, how failures travel along them and how the platform analyses them while stating what is uncertain. Concentration is the recurring theme.
- STEM 方法
- 7
- 该领域应用的规范方法
- 主题
- 8
- 该领域的主题页面
- 对口方
- 3
- 网络中共享领域的各平台
- 报告系列
- 2
- 它参与的 Reports 系列
Upstream dependencies.
Chips, data centres and cloud
Training and running large models depends on concentrated chip supply and a few cloud providers. See 算力与能源.
Physical resources
Data centres draw electricity and, for cooling, often water, linking AI growth to grids and local water stress.
Inputs and labour
Training data, human feedback and annotation, often provided by low-paid workers, shape model behaviour.
领域概览。
团队跟踪该主题时会使用的界面：其指标、状态与决策。名称为示例，数值仅作示意。
Downstream consequences.
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Public services
Automated decisions in benefits, health, policing or migration affect rights directly. See AI 与公共服务.
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安全
AI changes the speed and scale of cyber attack and defence, and models are themselves targets. See 人工智能与网络安全.
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金融
Shared models and data can make institutions behave alike under stress, a concern examined in 人工智能与金融模型风险 at The Global Risks Alliance (GRA).
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Information and trust
Generated content at scale affects the information environment and trust in institutions.
Common modes and correlated errors.
Because many applications are built on the same foundation models, a flaw or vulnerability can appear simultaneously across sectors that otherwise share little. An update by one provider can change behaviour in thousands of downstream 系统. Outages at a major cloud region can stop services that never knew they shared it.
Social effects travel more slowly but last longer. Errors concentrated on particular groups erode trust, and changes in work and skills reshape labour markets over years. See 劳动与社会影响.
How dependencies are analysed.
The platform maps dependencies using system analysis, supply-chain mapping and incident studies, with 概率与统计 to express uncertainty and cybersecurity and cryptography for threat modelling. A dependency map is not a forecast: it shows how 系统 are connected and how failure could travel, and each link carries its evidence label. Governance responses are convened by 人工智能与数字治理 at The Global Risks Forum (GRF).
参见 方法与不确定性.
问题与解答。
Why does concentration matter?
How significant is AI’s energy use?
Are specific providers’ weaknesses published?
Who governs these dependencies?
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报告 is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
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