Artificial Intelligence and Autonomous Systems
The Global Centre for Risk and Innovation (GCRI)
AI systems 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.
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Dependencies and consequences in Artificial Intelligence and Autonomous Systems describe how artificial intelligence as well as data and compute connect to other systems through physical, economic, social and ecological links, and how stress travels along them toward fields such as those of Innovation Nexus and AI and Digital Governance. Every dependency map states its assumptions, data and uncertainty; it describes connections, not a forecast.
Fig.How GCRI, GRF and GRA share one field while the authorities decide. Design, not a record.
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Training and running large models depends on concentrated chip supply and a few cloud providers. See Compute and energy.
Data centres draw electricity and, for cooling, often water, linking AI growth to grids and local water stress.
Training data, human feedback and annotation, often provided by low-paid workers, shape model behaviour.
Automated decisions in benefits, health, policing or migration affect rights directly. See AI and public services.
AI in grid management, logistics and industrial control can propagate errors into physical systems. See Autonomous physical systems.
AI changes the speed and scale of cyber attack and defence, and models are themselves targets. See AI and cyber security.
Shared models and data can make institutions behave alike under stress, a concern examined in AI and Financial Model Risk at The Global Risks Alliance (GRA).
Generated content at scale affects the information environment and trust in institutions.
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 systems. 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 Labour and social effects.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Interdependency | A two-way dependency between infrastructures, such as power needing telecoms for control while telecoms need power to run. |
| Cascading risk | Risk that a failure in one system or component triggers failures in others that depend on it. |
| Compound event | Two or more hazards or drivers occurring together or in sequence whose joint impact exceeds what either would cause alone, such as drought followed by intense rain; IPCC assessments use the term. |
| Dependency mapping | Tracing the people, processes, technology, facilities and suppliers a service relies on. |
The platform maps dependencies using system analysis, supply-chain mapping and incident studies, with probability and statistics to express uncertainty and cybersecurity and cryptography for threat modelling. A dependency map is not a forecast: it shows how systems are connected and how failure could travel, and each link carries its evidence label. Governance responses are convened by AI and Digital Governance at The Global Risks Forum (GRF).
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.