Artificial Intelligence and Autonomous Systems
The Global Centre for Risk and Innovation (GCRI)
Governments use algorithms to target benefits, detect fraud, triage health care, allocate inspections, translate and answer citizens' questions. When these systems are wrong, opaque or biased, people lose services and rights they depend on, often without knowing why. This page sets out how public sector AI succeeds and fails, how it is assessed and made accountable, and what evidence serves the agencies, auditors and citizens involved.
AI and public services · Model registry · Release gate
| M-843 triage assistant | Public service | High | 22 of 24 suites | Blocked |
| M-266 flood nowcast | Science | Medium | 11 of 12 | Released |
| M-756 document search | Internal | Low | 8 of 8 | Released |
| M-769 agent planner | Agentic | High | 12 of 16 | Evaluating |
vs release threshold
Fig.Model registry. Design wireframe with placeholder names and illustrative values, not a record or a live system.
AI in public services is the use of algorithms by governments to target benefits, detect fraud, triage care, allocate inspections and answer citizens. Errors fall hardest on people who rely most on services, historical data can carry bias and opacity blocks challenge, so agencies must remain accountable.
Night-time lights and population density, the footprint of electrified, connected activity, framed on East Asia; each layer is a dated NASA product.
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Connected activity
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Natural Earth
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Night-time lights and population density, the footprint of electrified, connected activity, framed on East Asia.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
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Public accountability, access and rights. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
People who depend most on public services are often those most affected by automated decisions, and least able to challenge them.
Models trained on past decisions can reproduce discrimination present in those decisions, even when protected characteristics are excluded.
When people cannot see that an algorithm was used or why it produced a result, they cannot contest it, and errors persist.
Procuring a system from a vendor does not transfer responsibility. Agencies must be able to explain, audit and override algorithmic decisions.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Automated decision-making | Decisions about people that are made wholly or partly by algorithms, with or without human review. |
| Algorithmic impact assessment | A structured review of an algorithm's purpose, data, risks to rights and safeguards. |
| Algorithm register | A public record of algorithmic tools used by government bodies. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Public algorithm registers | count of systems registered | per update | Government algorithm registers | OFFICIAL |
| Algorithmic impact assessments completed | percent of deployed systems | annual | Agency records | DECLARED |
| Error rate by population group | difference in error rates | per audit | Algorithm audits | OBSERVED |
| Decisions appealed and overturned | count and percent | annual | Appeals records | OFFICIAL |
| Systems with human review of adverse decisions | percent of systems | per review | Agency documentation | DECLARED |
| Audit findings on public sector algorithms | count by severity | per audit cycle | Audit office reports | REPORTED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| Government algorithm and AI registers, such as the UK Algorithmic Transparency Recording Standard records | Public records of algorithmic tools in use | Open, Open Government Licence where applicable |
| National audit office reports on algorithms | Independent audits of public sector AI | Public records, licence per office |
| OECD.AI Policy Observatory | Government AI policies and the AI Incidents Monitor | Public, OECD terms |
| Court and ombudsman decisions on automated decision-making | Legal findings on public sector algorithms | Public records |
| Canada Directive on Automated Decision-Making and its impact assessment tool | A model for impact assessment requirements | Public, Government of Canada terms |
Artificial Intelligence and Autonomous Systems prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Government agencies | Whether and how to deploy an algorithm in a public service | Impact assessments; audit methods |
| Auditors and ombudspersons | Where to investigate algorithmic harm | Registers; appeals data; audit evidence |
| Legislatures | What transparency and accountability to require | Comparative policy and case evidence |
Systems trained on historical administrative data inherit its biases and gaps. Risk-scoring tools can concentrate scrutiny on groups already over-represented in past enforcement. Small error rates become large numbers of wrongly affected people at national scale. When staff defer to the system, known as automation bias, errors are not caught.
Opacity compounds harm: if neither officials nor citizens can see why a decision was made, it is hard to contest and slow to correct. Access barriers arise when services move online without alternatives.
Structured assessment of a system's purpose, data, affected groups and safeguards before deployment, using social and behavioural science. Quality depends on honest completion.
Disaggregated error rates by group, with probability and statistics. Different fairness definitions can conflict, so trade-offs must be made explicit.
Designing decision notices and appeal routes people can use. Technical explanations do not by themselves make a decision contestable.
Ongoing checks of outcomes and complaints after deployment.
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.