GCRI · 人工智能与自主系统
AI 与公共服务
Governments use algorithms to target benefits, detect fraud, triage health care, allocate inspections, translate and answer citizens’ questions. When these 系统 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.
工作如何开展。
模型注册库
AI and public services · Model registry · Release gate
模型
| M-843 triage assistant | 公共服务 | 高 | 22 of 24 suites | 已阻止 |
| M-266 flood nowcast | 科学 | 中 | 12 中的 11 | 已发布 |
| M-756 document search | 内部 | 低 | 8 of 8 | 已发布 |
| M-769 agent planner | 智能体式 | 高 | 12 of 16 | 评估中 |
模型卡
- 预期用途
- 分诊辅助，由人决定
- 不在范围内
- 资格决定
- 已知局限
- 在罕见案例上的准确率较低
- 负责人
- 模型所有者
事件日志
- D-13INC摘要中出现虚构引用
- D-6MIT已增加检索依据
- D-2INC通过上传文件的提示注入
- D-1MIT工具权限已收窄
Evaluation scores
vs release threshold
图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.
模型、其假设、数据和不确定性，以科学家或工程师会核查的形式呈现。结构遵循已发表的方法。所取数值仅为示意，不是研究发现。
系统模型
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 证据简报系列. 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.
衡量什么，以及衡量得如何。
每个指标都注明其单位、频率、背后的来源类型以及数值将附带的证据标签。此处不显示数值：数值由计划依据其自身权限提供。
| 指标 | 单位 | 节奏 | 来源类型 | 标签 |
|---|---|---|---|---|
| Public algorithm registers | count of 系统 registered | per update | Government algorithm registers | 已发布统计 |
| Algorithmic impact assessments completed | percent of deployed systems | 年度 | 机构记录 | 已声明 |
| Error rate by population group | difference in error rates | per audit | Algorithm audits | 观测 |
| Decisions appealed and overturned | count and percent | 年度 | Appeals records | 已发布统计 |
| Systems with human review of adverse decisions | percent of systems | 每次审查 | Agency documentation | 已声明 |
| Audit findings on public sector algorithms | count by severity | per audit cycle | Audit office reports | 已报告 |
证据来自何处。
具名公共数据集与机构记录，各自注明许可类别。依协议获得的来源仅按其条款使用，绝不再次发布。
| 来源 | 它提供什么 | 许可类别 |
|---|---|---|
| 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 政策观察站 | 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 | 公共记录 |
| Canada Directive on Automated Decision-Making and its impact assessment tool | A model for impact assessment requirements | Public, Government of Canada terms |
由谁决定，以及证据服务于什么。
人工智能与自主 系统 prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| 谁来决定 | 问题 | 本页面服务的证据 |
|---|---|---|
| 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 |
| 立法机构 | What transparency and accountability to require | Comparative policy and case evidence |
该主题产生的产出
- 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.
系统 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
影响评估
Structured assessment of a system’s purpose, data, affected groups and safeguards before deployment, using 社会与行为科学. Quality depends on honest completion.
- 02
Fairness and error analysis
Disaggregated error rates by group, with 概率与统计. 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.
Where this page’s references lead.
阅读本页时所对照的机构和文件。状态和版本会变化；每个链接都指向发布机构。
问题与解答。
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?
从本页贡献证据。
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.
- 电子邮件网关 GW-GCRI从本页发送证据、更正、案例、方法、审查、翻译或概况投稿。它会带上本页标签送达，由负责的编辑接收。
- Zenodo开放存缴存缴并提交到社区用您的 ORCID iD 在 Zenodo 上发布您自己的记录，然后提交到那里的 Reports 社区。由策展人审核；DOI 仍归您所有。
- 报告GCRI查看所有贡献方式贡献类型、各自的评审方式、您获得的署名、许可协议，以及绝不接受的内容。
切勿发送个人数据、敏感基础设施细节或您无权分享的任何内容。贡献在使用前会经过审查；发送贡献不赋予署名权或编辑权，赞助也绝不能换取研究结论。
法律文件。
本网站上的地图、数据和图表仅作为背景和一般信息展示。它们不是建议、预测或预警；信息的局限见免责声明和数据说明。