GCRI · Zîrekiya çêkirî û pergalên xweser
AI û xizmetên giştî
Governments use algorithms to target benefits, detect fraud, triage health care, allocate inspections, translate and answer citizens’ questions. When these pergalan 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.
Xebat çawa dimeşe.
Tomara modelan
AI and public services · Model registry · Release gate
Model
| M-843 triage assistant | Xizmeta giştî | Bilind | 22 of 24 suites | Hatiye asteng kirin |
| M-266 flood nowcast | Zanist | Navîn | 11 ji 12 | Hatiye weşandin |
| M-756 document search | Navxweyî | Kêm | 8 of 8 | Hatiye weşandin |
| M-769 agent planner | Ajanî | Bilind | 12 of 16 | Tê nirxandin |
Karta modelê
- Bikaranîna armanckirî
- piştgiriya hilbijartina pêşîniyê, mirov biryarê dide
- Li derveyî çarçoveyê
- biryarên mafdariyê
- Sînorên naskirî
- rastbûna kêmtir li ser rewşên kêm
- Xwedî
- Xwediyê modelê
Têketina bûyeran
- D-13INCÇavkaniya xeyalî di kurteyê de
- D-6MITBingehkirina vegerandinê hat zêdekirin
- D-2INCDerzîya fermanê bi pelê barkirî
- D-1MITDestûra amûrê hate teng kirin
Evaluation scores
vs release threshold
ŞeklModel registry: example names and illustrative values, not a record or a live system.
AI and public services: how the risk develops.
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.
Errors fall on the vulnerable
People who depend most on public services are often those most affected by automated decisions, and least able to challenge them.
Historical data carry bias
Models trained on past decisions can reproduce discrimination present in those decisions, even when protected characteristics are excluded.
Opacity blocks challenge
When people cannot see that an algorithm was used or why it produced a result, they cannot contest it, and errors persist.
Accountability stays with the agency
Procuring a system from a vendor does not transfer responsibility. Agencies must be able to explain, audit and override algorithmic decisions.
How AI and public services is modelled, measured and tested.
Model, texmînên wî, daneyên wî û nediyariya wî, bi şeweyê ku zanyar an endezyar kontrol bike. Avahî rêbazên weşandî dişopînin. Nirxên hatine xêzkirin nimûneyî ne, ne encam in.
Modela pergalê
The couplings between the systems in AI and public services: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
AI in public services touches the domains people depend on.
Public service AI studies are designed for the rêzeya Kurteyên Delîlê. The EU AI Act classifies many public-service uses as high-risk; UNESCO’s Recommendation (2021) and WHO guidance (2021) set ethical expectations.
- Algorithmic impact assessments a body publishes.
- Accounts from people affected by automated decisions.
What is measured, and how well.
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 | Nîşan |
|---|---|---|---|---|
| Public algorithm registers | count of pergalan registered | per update | Government algorithm registers | Statîstîkên weşandî |
| 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 | ÇAVDÊRÎKIRÎ |
| Decisions appealed and overturned | count and percent | annual | Appeals records | Statîstîkên weşandî |
| 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 |
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Çavkanî | 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 |
Who decides, and what the evidence serves.
Zîrekiya çêkirî û Pergal prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Kî biryarê dide | 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 |
Outputs this topic produces
- Impact assessment templates for public sector AI
- Audit methods for disaggregated error and fairness
- Transparency record templates
- Case studies of public sector algorithm failures and fixes
What public-sector technologists ask.
- Is the system accurate for the whole population it serves, including minority groups?
- Can affected people understand a decision, challenge it and get it corrected?
- Who is accountable when an automated decision is wrong?
- Does automation improve access, or does it create new barriers for people without digital skills or connections?
How automated decisions go wrong.
Pergal 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 public servants 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.
Evidence for accountable systems.
- 01
Impact assessment
Structured assessment of a system’s purpose, data, affected groups and safeguards before deployment, using zanista civakî û reftarî. Quality depends on honest completion.
- 02
Fairness and error analysis
Disaggregated error rates by group, with îhtîmal û statîstîk. Different fairness definitions can conflict, so trade-offs must be made explicit.
- 03
Explanation and contestability
Designing decision notices and appeal routes people can use. Technical explanations do not by themselves make a decision contestable.
- 04
Audit and monitoring
Ongoing checks of outcomes and complaints after deployment.
How it is studied, and what the methods cannot say.
- 01Zanistên civakî û tevgerîHuman oversight studies · Affected community research · Workplace adoption studiesApplied here
- 02Endezyariya nirxandin û teminata zîrekiya çêkirîAssurance cases for AI systems · Structured red teaming · Post-deployment monitoringApplied here
- 03Îhtîmal û statîstîkUncertainty on benchmark scores · Calibration assessment · Audit samplingApplied here
- 04Zîrekiya çêkirî û fêrbûna makîneyêDistribution shift analysis · Fine-tuning for a specific use · Interpretability methodsApplied here
Where this page’s references lead.
Sazî û belgeyên ku ev rûpel li hember wan tê xwendin. Rewş û çap diguherin; her girêdan diçe ba saziya derxistinê.
Pirs û bersiv.
What is automated decision-making?
What is an algorithmic impact assessment?
Can people challenge an algorithmic decision?
Should public services use AI at all?
What is automation bias?
Can affected people use the platform’s work?
Does the platform evaluate specific government systems?
Ji vê rûpelê delîl bidin.
rêzeya Pergalên Stratejîk 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.
- E-nameDergeh GW-GCRIJi vê rûpelê bişîninDelîl, rastkirin, doz, rêbaz, nirxandin, wergerandin an têketina profîlê. Bi nîşana vê rûpelê digihîje, ji ber vê yekê edîtorê xwediyê wê wê bistîne.
- ZenodoDepoya vekirîWê razînin û ji civakê re bişîninTomara xwe li Zenodo bi ORCID iD ya xwe biweşînin, paşê wê li wir ji civaka Reports re bişînin. Kurator wê vedikolin; DOI ya we dimîne.
- rêzeya Pergalên StratejîkGCRIHemû rêyên beşdariyê bibîneCureyên beşdariyê, her yek çawa tê nirxandin, tu çi navlêkirinê distînî, lîsans û tiştê ku qet nayê qebûlkirin.
Tu carî daneyên şexsî, hûrgiliyên binesaza hesas an tiştekî ku mafê we yê parvekirinê tune ne neşînin. Beşdarî berî bikaranînê tê nirxandin; şandina wê nivîskarî an mafên edîtorî nade û sponsorî tu carî encamekê nakire.
Rêbazek, danehevek an pirsek bîne.
Agahî û pirs îro vekirî ne. Pirsek referansekê distîne û gava piştî wê dide nasîn; serlêdan, drav, veqetandina hesabkirinê û tomarkirin tenê dema ku tîm, mercên wê û kapasîteya vekolînê hebin vedibin.
Belgeyên hiqûqî.
Nexşe, dane û jimarên vê malperê ji bo çarçoveyê û agahiyên giştî têne nîşandan. Ew ne şîret, ne pêşbînî û ne hişyarî ne; sînorên agahiyê di Daxuyaniyên Berpirsiyariyê û Daxuyaniyên Daneyê de hatine diyarkirin.