GCRI · هوش مصنوعی و سامانه‌های خودمختار
هوش مصنوعی و خدمات عمومی
Governments use algorithms to target benefits, detect fraud, triage health care, allocate inspections, translate and answer citizens’ questions. When these سامانه‌ها 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.
چگونه کار اجرا می‌شود.
ثبت‌خانهٔ مدل
AI and public services · Model registry · Release gate
مدل‌ها
| M-843 triage assistant | خدمات عمومی | بالا | 22 of 24 suites | مسدود |
| M-266 flood nowcast | علم | متوسط | 11 از 12 | منتشرشد |
| M-756 document search | داخلی | پایین | 8 of 8 | منتشرشد |
| M-769 agent planner | عامل‌محور | بالا | 12 of 16 | در حال ارزیابی |
کارت مدل
- کاربرد مورد نظر
- پشتیبانی غربالگری، انسان تصمیم می‌گیرد
- خارج از دامنه
- تصمیم‌های صلاحیت
- محدودیت‌های شناخته‌شده
- دقت کمتر در موارد نادر
- مالک
- مالک مدل
سیاههٔ رخداد
- D-13INCارجاع ساختگی در چکیده
- D-6MITزمین‌گذاری بازیابی افزوده شد
- D-2INCتزریق فرمان از طریق پروندهٔ بارگذاری‌شده
- D-1MITمجوز ابزار محدودتر شد
Evaluation scores
vs release threshold
شکل.Model 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.
مدل، مفروضات، داده‌ها و عدم قطعیت آن، به شکلی که دانشمند یا مهندس بررسی می‌کند. ساختارها از روش‌های منتشرشده پیروی می‌کنند. مقادیر ترسیم‌شده تبیینی‌اند، نه یافته.
مدل سامانه
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 مجموعهٔ گزارش‌های کوتاه شواهد. 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 | دورهٔ تکرار | Source type | برچسب |
|---|---|---|---|---|
| Public algorithm registers | count of سامانه‌ها registered | per update | Government algorithm registers | آمارهای منتشرشده |
| 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 | مشاهده‌شده |
| Decisions appealed and overturned | count and percent | annual | Appeals records | آمارهای منتشرشده |
| 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 | گزارش‌شده |
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.
| منبع | 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.
هوش مصنوعی و سامانه‌ها prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| چه کسی تصمیم می‌گیرد | 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.
سامانه‌ها 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 علوم اجتماعی و رفتاری. Quality depends on honest completion.
- 02
Fairness and error analysis
Disaggregated error rates by group, with احتمال و آمار. 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.
- 01علوم اجتماعی و رفتاریHuman oversight studies · Affected community research · Workplace adoption studiesApplied here
- 02مهندسی ارزیابی و اطمینان‌بخشی هوش مصنوعیAssurance cases for AI systems · Structured red teaming · Post-deployment monitoringApplied here
- 03احتمال و آمارUncertainty on benchmark scores · Calibration assessment · Audit samplingApplied here
- 04هوش مصنوعی و یادگیری ماشینDistribution shift analysis · Fine-tuning for a specific use · Interpretability methodsApplied here
Where this page’s references lead.
نهادها و اسنادی که این صفحه بر پایهٔ آن‌ها خوانده می‌شود. وضعیت و ویراست‌ها تغییر می‌کنند؛ هر پیوند به نهاد صادرکننده می‌رود.
پرسش و پاسخ.
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?
شواهد را از همین صفحه ارسال کنید.
گزارش‌ها 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.
- ایمیلدرگاه GW-GCRIاز همین صفحه ارسال کنیدشواهد، یک اصلاحیه، یک مورد، یک روش، یک داوری، یک ترجمه یا ورودی نمایه. با برچسب این صفحه میرسد تا ویراستار مسئول آن را دریافت کند.
- Zenodoسپردهٔ بازآن را واگذار کنید و به جامعه ارائه دهیدسابقهٔ خود را با شناسهٔ ORCID خود در Zenodo منتشر کنید و سپس آن را در همانجا به جامعهٔ Reports ارسال کنید. گزینشگران آن را داوری می‌کنند؛ DOI از آنِ شما میماند.
- گزارش‌هاGCRIهمهٔ راههای مشارکت را ببینیدانواع مشارکت، نحوهٔ بررسی هر یک، اعتباری که دریافت میکنید، مجوز و آنچه هرگز پذیرفته نمیشود.
هرگز داده شخصی، جزئیات حساس زیرساخت یا چیزی را که حق اشتراکش را ندارید نفرستید. هر مشارکت پیش از استفاده داوری می‌شود؛ ارسال آن حق مؤلفی یا ویرایشی نمی‌دهد و حمایت مالی هرگز یافته‌ای را نمی‌خرد.
یک روش، یک مجموعه‌داده یا یک پرسش بیاورید.
اطلاعات و درخواست‌ها امروز باز است. هر درخواست یک شمارهٔ پیگیری می‌گیرد و گام بعدی را مشخص می‌کند؛ درخواست‌های عضویت، پرداخت‌ها، تخصیص رایانش و ثبت‌نام‌ها تنها زمانی باز می‌شوند که تیم، شرایط و ظرفیت داوری آن‌ها فراهم باشد.
اسناد حقوقی.
نقشه‌ها، داده‌ها و ارقام این سایت برای زمینه و اطلاعات عمومی نشان داده می‌شوند. توصیه، پیش‌بینی یا هشدار نیستند؛ حدود این اطلاعات در سلب مسئولیت‌ها و اعلامیه‌های داده بیان شده است.