[{"data":1,"prerenderedAt":724},["ShallowReactive",2],{"uc-public-service-translation":3,"uc-regulations":519},{"useCase":4,"evidence":205,"blitsAiDeployments":414,"benchmarks":415,"indicative":425,"related":428,"indexability":517,"includeUnpublished":211},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":27,"audience":33,"autonomy":34,"adoptionStage":35,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":50,"macroEstimates":85,"feasibility":86,"implementation":98,"risk":141,"blitsAi":181,"faq":183,"related":193,"datePublished":199,"dateModified":199,"lastVerified":200,"changelog":201,"slug":204},"AI translation and interpretation for multilingual public services","Public service translation","AI translation and interpretation for government","Governments use AI to translate documents and conversations. EU eTranslation translated 891 million pages in 2025; the State Department pilots it at visa windows.","published","AI that translates government content, documents and conversations between officials and the public, in writing and in real time speech, so people can use public services in their own language, with human translators and interpreters reviewing what carries legal or safety weight.",[12,13,14,15],"government machine translation","AI interpretation for public services","language access AI","multilingual citizen service",[17],"government",[19,20,21],"citizen-services","customer-service","operations",[23,24,25,26],"translation","conversational-agent","speech-analytics","document-processing",[28,29,30,31,32],"web-chat","voice","kiosk","internal-tools","api","customer-facing","copilot","early-adopters","Every public service has residents who do not speak the official language well, and they tend to\nbe the people who most need services: new arrivals, disaster survivors, people in crisis.\nProfessional translation of documents is slow and costly, so agencies translate a few key pages\nand summarise the rest; interpreters are limited and take time to connect, especially for rarer\nlanguages; staff fall back on ad hoc translation, including free consumer apps, outside any\ngovernance.\n\nMachine translation and speech models now make many languages workable in seconds, but a\nmistranslated address in an emergency call, a symptom at a clinic or a sentence in an asylum\ninterview can change an outcome. The design question is where AI translation is enough and where\na human must check it.",[],"1. **Translate published content.** Web pages, letters and guidance are machine translated with\n   domain glossaries and style settings, then reviewed by a human for high impact texts.\n2. **Translate what the public sends.** Documents residents submit in other languages are\n   translated in full, with the original kept alongside for the case file.\n3. **Converse across languages.** Chat and voice assistants detect the resident's language and\n   answer in it, grounded in content in the official language.\n4. **Interpret live.** At counters, interview windows and on the phone, speech is transcribed,\n   translated and voiced or shown on screen for both sides, with a transcript kept when enabled.\n5. **Escalate to people.** Where the law or the stakes require it, a certified interpreter or\n   translator takes over, and staff can call one in at any moment.",[40,41,42,43],"inclusion-and-access","speed","cost-to-serve","customer-experience",[45,46,47,48,49],"accuracy","processing-time-reduction","cost-reduction","interactions-handled","users-served",{"referenceOrg":51,"inputs":52,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"An agency that commissions translation of 20,000 incoming documents a year",[53,59,67,74],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"documents","Incoming documents translated per year",20000,"documents per year","The reference agency. Editorial assumption, replace with your own volume.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65,"sourceUrl":66},"humanCost","Cost of human translation per document",30,50,"USD per document","FEMA puts its current cost at approximately USD 40 per document; the range brackets that figure.","https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"machineShare","Share of documents where machine translation plus light review is enough",0.5,0.8,"fraction of documents","Editorial assumption; the rest still need full human translation.",{"key":75,"label":76,"low":77,"high":78,"unit":64,"note":79},"reviewCost","Cost of light human review of a machine translation",5,10,"Editorial assumption. Replace with your own review cost.","documents * machineShare * (humanCost - reviewCost)","USD","per year","Document translation cost avoided","Covers incoming documents only. It leaves out faster case decisions, interpreter costs on calls and at counters, the value of wider language access and the cost of the translation service.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":93},"medium","Machine translation itself is widely available, in some cases free to public administrations; the work is in glossaries, quality review for high impact texts, keeping originals with translations, and integrating speech translation into counters, phones and interview rooms without breaking legal rights to an interpreter.",[90,91,92],"Language demand by service and channel, to pick languages","Domain glossaries and approved translations of key terms","Rules on which document and conversation types need certified human translation",[94,95,96,97],"Content management system and website (translation by API)","Case management and document stores, to keep originals and translations together","Telephony, counters and interview rooms for speech translation","Contact centre and chat channels for multilingual assistants",{"steps":99,"guardrails":115,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[100,103,106,109,112],{"title":101,"detail":102},"Classify content and conversations by stakes","Decide which texts and conversations can use machine translation alone, which need human review, and which require a certified interpreter by law.",{"title":104,"detail":105},"Use a governed service, not phones","Replace ad hoc free apps with an approved service with data protection terms, such as the European Commission's eTranslation for eligible administrations.",{"title":107,"detail":108},"Build glossaries","Load approved translations of programme names and legal terms so the same concept is translated the same way everywhere.",{"title":110,"detail":111},"Keep the original next to the translation","Store both in the case file so a reviewer can check a disputed phrase. FEMA plans to keep the original and the translation together in survivors' files as substantiating documents.",{"title":113,"detail":114},"Pilot live interpretation with staff","Start with short, structured interactions (such as visa windows or Spanish 911 calls), keep transcripts and measure repeat questions and escalations to interpreters.",[116,117,118,119,120],"Certified human interpretation where the law requires it, and always on request","Original text or audio retained alongside every translation used in a decision","Glossaries for programme names and legal terms, maintained by the service","Data protection terms that keep public data out of model training","Visible notice to the public that a translation is machine generated","Human translators review high impact published texts and any translation used in a decision; staff can call an interpreter at any point in a conversation. Language leads sample machine translations each month by language and correct the glossary.",[123,124,125,126,127],"Quality scores by language on a monthly human reviewed sample","Time from document receipt to translated file","Share of conversations escalated to a human interpreter, by language","Translation cost per document and per call","Complaints and corrections linked to translation",[129,132,135,138],{"title":130,"detail":131},"Errors that change a case","The Guardian reported machine translation errors that affected US asylum applications. Use certified interpreters for interviews that decide status, and keep transcripts.",{"title":133,"detail":134},"Rare languages quietly worse","Translators quoted by The Guardian in 2023 said AI tools are particularly unreliable for less documented languages, and that major tools did not offer some languages at all. Measure quality by language and route rare languages to people.",{"title":136,"detail":137},"Shadow translation","Staff paste case data into free consumer apps. Provide an approved tool and block the rest.",{"title":139,"detail":140},"Inconsistent terminology","The same benefit gets different names on different pages. Maintain glossaries, as the IRS does with its Publication 850 glossary of English and Spanish tax terms.",{"euAiAct":142,"regulations":145,"guidance":152,"controls":170,"incidents":176},{"tier":143,"basis":144},"context-dependent","Assistants that talk with residents must tell people they are interacting with AI (Article 50(1)), and AI generated text published to inform the public on matters of public interest must be disclosed unless it has had human review under editorial responsibility (Article 50(4)). Internal translation that neither talks with people nor is published carries no specific obligation. Translation can also sit inside an Annex III process, such as examining asylum, visa or residence permit applications (point 7(c)) or evaluating emergency calls and dispatching emergency services (point 5(d)). Whether the translation component is itself high risk depends on its intended purpose (Article 6(3) exempts systems that only perform a narrow procedural task); either way it should be governed with that high risk process.",[146,147,148,149,150,151],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","uk-gdpr","uk-atrs",[153,159,165],{"title":154,"issuer":155,"region":156,"url":157,"note":158},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","Sets when people must be told they are interacting with AI, when providers must mark generated content, and when deployers must disclose AI generated text published to inform the public. Applies from 2 August 2026.",{"title":160,"issuer":161,"region":162,"url":163,"note":164},"Limited English Proficiency (notice on the suspension of lep.gov)","US Department of Justice, Civil Rights Division","north-america","https://www.justice.gov/crt/limited-english-proficiency","The Department of Justice has temporarily suspended lep.gov to implement Executive Order 14224, pending an internal review; its language access materials will be replaced when new guidance is issued. Check the current federal position before relying on older guidance.",{"title":166,"issuer":167,"region":156,"url":168,"note":169},"AI translation and language tools","European Commission","https://commission.europa.eu/resources-partners/etranslation_en","Describes eTranslation and related tools available free to eligible public administrations.",[171,172,173,174,175],"Language access policy stating where machine translation is allowed","Register entry for translation tools used in decisions","Data processing agreement that excludes training on public data","Monthly quality sampling by language","Staff training on when to call a human interpreter",[177],{"title":178,"url":179,"note":180},"Lost in AI translation: growing reliance on language apps jeopardizes some asylum applications","https://www.theguardian.com/us-news/2023/sep/07/asylum-seekers-ai-translation-apps","Reporting on US asylum cases harmed by AI translation, such as a city name translated literally in an application; volunteers describe applications denied after mistranslations.",{"howToBuild":182},"On Blits.ai bots are **multi language**: each bot has a language list, flows hold localized\ncontent per language, the platform detects the resident's language and can switch mid conversation, and\n**machine translation** runs through Amazon, Google, IBM or Microsoft. An **AI agent** grounded\nin a **knowledge base** in the official language can answer in the resident's language, with\nstrong **Arabic** support (normalization, Arabic voices and regional Arabic models).\n\nOn the phone, **speech to text across nine providers** and **text to speech across thirteen**\nlet a **voice agent** converse in many languages, and **self hosted transcription with language\ndetection** keeps audio on Blits.ai infrastructure. **PII masking**, **guardrails**, **human\nhandover** to a staff member or interpreter, and **EU and UAE data residency** complete the\ngoverned setup; **test suites** check answers per language.",[184,187,190],{"question":185,"answer":186},"Is machine translation good enough for public services?","For information and routine conversations it can be, with glossaries and regular quality checks; the European Commission's eTranslation, free to eligible public administrations, translated 891 million pages in 2025. For decisions, treat it as a draft and keep the original: FEMA plans to translate survivors' documents in full and store the original and the translation together as substantiating documents in the survivor's file, and the State Department's citizen services pilot is assistive only, not a replacement for certified interpreters where they are required.",{"question":188,"answer":189},"Can AI interpret live at a counter or on the phone?","It is being piloted and used. The State Department is piloting live interpretation at the visa interview window, and Baltimore 911 operators can dial in an automated Spanish voice translator instead of a third party interpreter.",{"question":191,"answer":192},"Which languages can assistants cover?","Many. Montgomery County's Monty 2.0 answers in 140 languages and Madrid's visitor assistant in more than 95. Quality varies by language, so measure it for the languages your residents speak.",[194,195,196,197,198],"citizen-information-assistant","immigration-and-visa-application-assistant","emergency-call-triage-support","benefits-eligibility-and-application-assistant","non-emergency-service-request-routing","2026-09-27","2026-09-26",[202],{"date":199,"note":203},"First published","public-service-translation",[206,230,248,278,313,332,363,390],{"title":207,"useCases":208,"organization":209,"vendors":213,"summary":214,"stage":215,"year":216,"channels":217,"languages":218,"metrics":220,"outcomeDisclosed":211,"sources":221,"verification":225,"grade":227,"id":228,"organizationSlug":229},"FEMA: machine translation of disaster survivors' documents for Individual Assistance",[204,197],{"name":210,"anonymized":211,"country":212,"region":162,"industry":17},"Federal Emergency Management Agency",false,"US",[],"FEMA plans to translate the full text of non English documents that disaster survivors submit with their Individual Assistance applications, instead of relying on a contractor's summary of each document. The agency expects faster case processing and a drop in cost from about USD 40 per document to pennies. Original and translation will both be stored in the survivor's file, as substantiating documents that support assistance determinations.","announced",2025,[31],[219],"en",[],[222],{"url":66,"title":223,"publisher":224},"2025 individually reported AI use cases (consolidated federal inventory data)","Office of Management and Budget (GitHub)",{"level":226,"checkedAt":200},"source-verified","B","fema-individual-assistance-document-translation",null,{"title":231,"useCases":232,"organization":233,"vendors":235,"summary":239,"stage":240,"year":216,"channels":241,"languages":242,"metrics":243,"outcomeDisclosed":211,"sources":244,"verification":246,"grade":227,"id":247,"organizationSlug":229},"US Department of State: live AI interpretation at consular windows (LCALA)",[195,204],{"name":234,"anonymized":211,"country":212,"region":162,"industry":17},"U.S. Department of State (Bureau of Consular Affairs)",[236],{"name":237,"role":238},"Microsoft","platform","Consular Affairs is piloting Live Consular AI Language Augmentation (LCALA): real time transcription and neural machine translation of the spoken exchange at the visa interview window, with translated audio and on screen text and an optional short time stamped transcript. The visa interview pilot is flagged high impact in the federal inventory; its entry notes that interviews last about three minutes and that misunderstandings can force repeat questions, delays or uneven outcomes. A second pilot, for Overseas Citizens Services and American Citizens Services, supports calls and in person interactions and is described as assistive only, not a replacement for certified interpreters where they are required.","pilot",[29,31],[],[],[245],{"url":66,"title":223,"publisher":224},{"level":226,"checkedAt":199},"us-department-of-state-consular-ai-interpretation",{"title":249,"useCases":250,"organization":252,"vendors":254,"summary":259,"stage":260,"year":261,"channels":262,"languages":263,"metrics":268,"outcomeDisclosed":211,"sources":269,"verification":275,"grade":227,"id":276,"organizationSlug":277},"IRS: neural machine translation for taxpayer content and case work",[204,251],"tax-questions-and-filing-assistant",{"name":253,"anonymized":211,"country":212,"region":162,"industry":17},"Internal Revenue Service",[255,257],{"name":256,"role":238},"Amazon Web Services (Amazon Translate)",{"name":258,"role":238},"SYSTRAN","The IRS Linguistic Policy, Tools and Services team uses a cloud machine translation application on AWS, with the IRS Publication 850 glossary of English and Spanish tax terms, to translate text and files between English and Spanish, Chinese, Korean and Vietnamese and speed up responses to taxpayers. Separately, staff use SYSTRAN neural translation, augmented with a domain dictionary and translation memories, to triage non English documents for relevance to case work and as a starting point for manual translation. Both appear in the federal AI inventory as in operation.","production",2020,[31],[219,264,265,266,267],"es","zh","ko","vi",[],[270,274],{"url":271,"title":272,"publisher":224,"date":273},"https://raw.githubusercontent.com/ombegov/2024-Federal-AI-Use-Case-Inventory/main/data/2024_consolidated_ai_inventory_raw_v2.csv","2024 consolidated AI use case inventory (raw data, version 2)","2025-01-23",{"url":66,"title":223,"publisher":224},{"level":226,"checkedAt":200},"irs-machine-translation","internal-revenue-service",{"title":279,"useCases":280,"organization":281,"vendors":282,"summary":286,"stage":287,"year":288,"channels":289,"languages":290,"metrics":296,"outcomeDisclosed":306,"sources":307,"verification":311,"grade":227,"id":312,"organizationSlug":229},"European Commission: eTranslation for public administrations",[204],{"name":167,"anonymized":211,"region":156,"industry":17},[283],{"name":284,"role":285},"European Commission (Directorate General for Translation)","in-house","eTranslation is the European Commission's neural machine translation service, launched in 2017, trained on EU professional translation data and offered free to eligible users such as public administrations. It translates text and documents, offers an EU formal or general style, and can translate websites through an API. The Commission says most of its language tools cover all 24 official EU languages and several more, including Arabic, Chinese and Ukrainian, and that eTranslation translated 891 million pages in 2025, up from 19 million in its first year. It now sits alongside related AI tools for summaries and draft replies.","scaled",2017,[32,31],[219,291,292,293,264,294,295],"fr","de","nl","it","pl",[297],{"kpi":48,"value":298,"unit":299,"qualifier":300,"period":301,"baseline":302,"claimant":303,"quote":304,"sourceUrl":305},891000000,"count","exact","pages translated in 2025","19 million pages in its first year after the 2017 launch","organization","In 2025, eTranslation translated 891 million pages.","https://translation.ec.europa.eu/language-data-and-ai-using-ai-break-down-language-barriers/ai-based-multilingual-services-using-eu-language-data-innovate_en",true,[308,309],{"url":168,"title":166,"publisher":167},{"url":305,"title":310,"publisher":284},"AI-based multilingual services: using EU language data to innovate",{"level":226,"checkedAt":200},"european-commission-etranslation",{"title":314,"useCases":315,"organization":316,"vendors":318,"summary":321,"stage":260,"year":216,"channels":322,"languages":323,"metrics":324,"outcomeDisclosed":211,"sources":325,"verification":329,"grade":330,"id":331,"organizationSlug":229},"Delaware County: automated Spanish text to voice translation on calls",[204],{"name":317,"anonymized":211,"country":212,"region":162,"industry":17},"Delaware County",[319],{"name":320,"role":238},"Prepared","Delaware County (Chief of Communications Anthony Mignogna) uses an automated, real time text to voice translation tool to communicate directly with Spanish speaking callers. The vendor reports that the county and other agencies have significantly reduced call processing time for Spanish calls; the detailed figures sit behind a download form and are not recorded here.",[29],[219,264],[],[326],{"url":327,"title":328,"publisher":320},"https://www.prepared911.com/case-studies/delaware-county-spanish-text-to-voice-translation","Delaware County Streamlines Spanish Call Processing with Text-to-Voice Translation",{"level":226,"checkedAt":200},"C","delaware-county-911-spanish-voice-translation",{"title":333,"useCases":334,"organization":336,"vendors":339,"summary":346,"stage":260,"year":347,"channels":348,"languages":349,"metrics":350,"outcomeDisclosed":211,"sources":351,"verification":361,"grade":330,"id":362,"organizationSlug":229},"City of Madrid (Madrid Destino): VisitMadridGPT multilingual visitor assistant",[204,194,335],"ai-visitor-and-tour-guide",{"name":337,"anonymized":211,"country":338,"region":156,"industry":17},"Madrid Destino","ES",[340,343],{"name":341,"role":342},"iUrban","integrator",{"name":344,"role":345},"Microsoft (Azure OpenAI Service)","model-provider","Madrid Destino, the city's municipal tourism office, runs VisitMadridGPT, a virtual assistant that answers visitors in more than 95 languages from the city's official, expert curated tourism site, available when physical offices are closed. The city analyses the questions to find the most requested topics and adjust its website content. According to the story, Madrid attracted 10.6 million visitors in 2023.",2024,[28],[],[],[352,356],{"url":353,"title":354,"publisher":355},"https://customers.microsoft.com/en-us/story/1831036907720807463-esmadrid-azure-openai-service-national-government-en-spain","Transforming tourism in Madrid with Azure OpenAI Service","Microsoft Customer Stories",{"url":357,"title":358,"publisher":359,"date":360},"https://www.eldiariodemadrid.es/articulo/madrid/visitmadridgpt-asistente-virtual-turistas-madrid/20240410141511074094.html","VisitMadridGPT, el asistente virtual para los turistas en Madrid","El Diario de Madrid","2024-04-10",{"level":226,"checkedAt":199},"madrid-destino-visitmadridgpt",{"title":364,"useCases":365,"organization":366,"vendors":368,"summary":372,"stage":260,"year":347,"channels":373,"languages":374,"metrics":375,"outcomeDisclosed":306,"sources":385,"verification":388,"grade":330,"id":389,"organizationSlug":229},"Montgomery County, Maryland: Monty 2.0 constituent chatbot",[194,198,204],{"name":367,"anonymized":211,"country":212,"region":162,"industry":17},"Montgomery County Government",[369,371],{"name":370,"role":238},"Zammo.ai",{"name":344,"role":345},"Montgomery County first launched Monty to relieve its 311 hotline during the pandemic, with 20 topics, and retired it when demand fell. Monty 2.0, built with Zammo.ai on Azure OpenAI Service and Azure AI Search, answers questions on more than 3,000 topics, with automatic translation into 140 languages, from the county's own knowledge base, and uses the county's geographic data to give address specific answers such as trash pickup days. It went through a seven month beta with a constituent focus group before the full launch in late 2024.",[28],[219],[376,382],{"kpi":48,"value":56,"unit":299,"qualifier":377,"period":378,"claimant":379,"quote":380,"sourceUrl":381},"at-least","since the beta deployment","vendor","Since its beta deployment, Monty 2.0 has facilitated more than 20,000 constituent conversations, achieving a 50% customer satisfaction rate and reducing unanswered queries from 35%–45% to just 10%–15%.","https://www.microsoft.com/en/customers/story/23066-montgomery-county-azure-open-ai-service",{"kpi":383,"value":63,"unit":384,"qualifier":300,"period":378,"claimant":379,"quote":380,"sourceUrl":381},"customer-satisfaction","percent",[386],{"url":381,"title":387,"publisher":355},"Montgomery County revolutionizes constituent experiences with an AI chatbot powered by Microsoft Azure OpenAI Service",{"level":226,"checkedAt":199},"montgomery-county-monty-chatbot",{"title":391,"useCases":392,"organization":393,"vendors":395,"summary":397,"stage":287,"year":398,"channels":399,"languages":401,"metrics":402,"outcomeDisclosed":306,"sources":409,"verification":412,"grade":330,"id":413,"organizationSlug":229},"Baltimore City 911: live transcripts, AI summaries, translation and automated QA",[196,204],{"name":394,"anonymized":211,"country":212,"region":162,"industry":17},"Baltimore City 911 (Emergency Communications)",[396],{"name":320,"role":238},"Baltimore's emergency communications centre answers about 1.4 million 911 calls a year. Since partnering with Prepared in early 2022, call takers see a live transcript, AI summaries and highlighted key details on every call, which helps with addresses and callers who are hard to understand. Non English calls are transcribed and translated in real time, and for Spanish calls operators can dial in an automated voice translator instead of a third party interpreter. Automated QA now reviews every call, where a contractor previously reviewed roughly 30%. The case study also carries the unattributed line that the tool is \"about 98% accurate\", without saying what was measured or how, so it is not recorded as an accuracy figure.",2022,[29,400],"agent-desktop",[219,264],[403],{"kpi":404,"value":405,"unit":384,"qualifier":300,"period":406,"claimant":379,"quote":407,"sourceUrl":408},"quality-score-uplift",12,"QA scores since automated QA was deployed; the QA method changed at the same time (sampling of about 30% of calls replaced by automated review of all calls), so before and after scores may not be comparable","Since deploying Automated QA, Baltimore has seen a 12% improvement in QA scores.","https://www.prepared911.com/case-studies/baltimore-911-call-processing-assistive-ai",[410],{"url":408,"title":411,"publisher":320},"Baltimore 911: Improving call processing efficiency with Assistive AI",{"level":226,"checkedAt":199},"baltimore-911-assistive-call-taking",0,[416],{"kpi":48,"label":417,"unit":299,"aggregate":211,"higherIsBetter":306,"n":418,"nUpTo":414,"median":419,"min":56,"max":298,"byClaimant":420,"vendorOnly":211,"points":422},"Interactions handled",2,445510000,{"organization":421,"vendor":421,"regulator":414,"independent":414},1,[423,424],{"evidenceId":312,"organization":167,"value":298,"qualifier":300,"claimant":303,"grade":227,"pooled":306},{"evidenceId":389,"organization":367,"value":56,"qualifier":377,"claimant":379,"grade":330,"pooled":306},{"low":426,"high":427},250000,640000,[429,456,469,485,502],{"slug":194,"title":430,"shortTitle":431,"definition":432,"status":9,"industries":433,"functions":434,"patterns":436,"audience":33,"autonomy":440,"adoptionStage":441,"evidenceCount":442,"publicEvidenceCount":443,"organizations":444,"bestGrade":227,"headline":452,"lastVerified":199,"indexable":306},"AI assistant for citizen information and government services","Citizen information assistant","An AI assistant that answers residents' and businesses' questions about government services in plain language, grounded only in official guidance with links to the source, points them to the right online service or office, and hands anything personal, urgent or outside its content to a human with the context attached.",[17],[19,20,435],"knowledge-management",[437,24,438,439],"rag-knowledge-assistant","voice-agent","classification-and-routing","supervised-agent","mainstream",11,9,[445,446,447,448,449,450,451,337,367],"Abu Dhabi Government (TAMM)","Driver and Vehicle Licensing Agency","Estonian Information System Authority (RIA)","Foreign, Commonwealth and Development Office","Gemeente Tilburg","Government Digital Service","Government of the City of Buenos Aires",{"kpi":45,"label":453,"unit":384,"n":421,"nUpTo":414,"kind":454,"value":455,"qualifier":377,"claimant":303,"organization":448,"vendorReported":211},"Accuracy","reported",76,{"slug":195,"title":457,"shortTitle":458,"definition":459,"status":9,"industries":460,"functions":461,"patterns":463,"audience":33,"autonomy":34,"adoptionStage":35,"evidenceCount":464,"publicEvidenceCount":464,"organizations":465,"bestGrade":227,"headline":229,"lastVerified":199,"indexable":306},"AI for immigration and visa applications, from applicant questions to case preparation","Immigration and visa application assistant","AI that helps applicants understand immigration and visa requirements and submit complete applications, and helps immigration staff prepare cases by extracting form data, classifying evidence, routing applications and supporting interviews, while every grant or refusal is decided by an officer against the immigration rules.",[17],[19,462,21],"case-management",[24,26,439,23],4,[466,234,467,468],"Home Office (Visa, Status and Information Services)","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services",{"slug":196,"title":470,"shortTitle":471,"definition":472,"status":9,"industries":473,"functions":475,"patterns":476,"audience":479,"autonomy":480,"adoptionStage":35,"evidenceCount":481,"publicEvidenceCount":481,"organizations":482,"bestGrade":227,"headline":229,"lastVerified":199,"indexable":306},"AI support for emergency call triage (112 and 911)","Emergency call triage support","AI that supports emergency call takers and dispatchers during 112 and 911 calls, with live transcription, translation, summaries, location cues and alerts for critical conditions such as cardiac arrest, while the call taker keeps every triage and dispatch decision.",[17,474],"healthcare",[19,21],[25,23,477,478],"summarization","prediction-and-scoring","employee-facing","assist",3,[394,483,484],"Copenhagen Emergency Medical Services","Galt Police Department",{"slug":197,"title":486,"shortTitle":487,"definition":488,"status":9,"industries":489,"functions":490,"patterns":491,"audience":33,"autonomy":34,"adoptionStage":35,"evidenceCount":492,"publicEvidenceCount":492,"organizations":493,"bestGrade":227,"headline":500,"lastVerified":200,"indexable":306},"AI assistant for benefits eligibility questions and applications","Benefits eligibility and application assistant","An AI assistant that helps people understand which public benefits and grants may apply to them, explains the rules and documents in plain language, guides them through the application and checks it for completeness, while the eligibility decision stays with the agency's rules and caseworkers.",[17],[19,462,20],[24,437,438,26],7,[494,495,210,496,497,498,499],"Department for Work and Pensions","Federal Student Aid (U.S. Department of Education)","Gemeente Nissewaard","Leeds City Council","Région Provence-Alpes-Côte d'Azur (Région Sud)","YoungWilliams",{"kpi":45,"label":453,"unit":384,"n":418,"nUpTo":414,"kind":454,"value":501,"qualifier":300,"claimant":303,"organization":494,"vendorReported":211},97,{"slug":198,"title":503,"shortTitle":504,"definition":505,"status":9,"industries":506,"functions":507,"patterns":508,"audience":33,"autonomy":440,"adoptionStage":35,"evidenceCount":492,"publicEvidenceCount":492,"organizations":510,"bestGrade":227,"headline":515,"lastVerified":200,"indexable":306},"AI for non emergency service requests and 311 routing","Non emergency service request routing","An AI agent on a city's 311 style phone, chat and messaging channels that answers routine municipal questions, takes service requests such as potholes, missed collections or broken street lights with the right location and details, creates the case in the work order system and routes anything urgent or complex to the right team.",[17],[19,20,462],[24,438,439,509],"agentic-workflow",[445,511,512,484,367,513,514],"London Borough of Barnet","City of Kelowna","Newcastle City Council","Rio de Janeiro City Data Office (Escritório de Dados)",{"kpi":45,"label":453,"unit":384,"n":421,"nUpTo":414,"kind":454,"value":516,"qualifier":300,"claimant":379,"organization":512,"vendorReported":306},80,{"indexable":306,"reasons":518},[],[520,525,530,537,543,549,555,562,570,577,584,590,596,603,609,614,621,626,632,638,643,649,654,659,664,670,677,682,688,695,701,707,713,718],{"id":146,"label":521,"issuer":155,"region":156,"url":522,"description":523,"useCases":524,"indexable":306},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":147,"label":526,"issuer":155,"region":156,"url":527,"description":528,"useCases":529,"indexable":306},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":149,"label":531,"issuer":532,"region":533,"url":534,"description":535,"useCases":536,"indexable":306},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":148,"label":538,"issuer":539,"region":162,"url":540,"description":541,"useCases":542,"indexable":306},"NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":544,"label":545,"issuer":155,"region":156,"url":546,"description":547,"useCases":548,"indexable":306},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":150,"label":550,"issuer":551,"region":156,"url":552,"description":553,"useCases":554,"indexable":306},"UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":556,"label":557,"issuer":558,"region":156,"url":559,"description":560,"useCases":561,"indexable":306},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":563,"label":564,"issuer":565,"region":566,"url":567,"description":568,"useCases":569,"indexable":306},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":571,"label":572,"issuer":573,"region":566,"url":574,"description":575,"useCases":576,"indexable":306},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":578,"label":579,"issuer":580,"region":533,"url":581,"description":582,"useCases":583,"indexable":306},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":585,"label":586,"issuer":587,"region":162,"url":588,"description":589,"useCases":583,"indexable":306},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":151,"label":591,"issuer":592,"region":156,"url":593,"description":594,"useCases":595,"indexable":306},"UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":597,"label":598,"issuer":599,"region":533,"url":600,"description":601,"useCases":602,"indexable":306},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":604,"label":605,"issuer":155,"region":156,"url":606,"description":607,"useCases":608,"indexable":306},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":610,"label":611,"issuer":155,"region":156,"url":612,"description":613,"useCases":608,"indexable":306},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":615,"label":616,"issuer":617,"region":162,"url":618,"description":619,"useCases":620,"indexable":306},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":622,"label":623,"issuer":155,"region":156,"url":624,"description":625,"useCases":405,"indexable":306},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":627,"label":628,"issuer":629,"region":162,"url":630,"description":631,"useCases":405,"indexable":306},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":633,"label":634,"issuer":635,"region":533,"url":636,"description":637,"useCases":405,"indexable":306},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":639,"label":640,"issuer":155,"region":156,"url":641,"description":642,"useCases":442,"indexable":306},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":644,"label":645,"issuer":646,"region":162,"url":647,"description":648,"useCases":442,"indexable":306},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":650,"label":651,"issuer":565,"region":566,"url":652,"description":653,"useCases":78,"indexable":306},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":655,"label":656,"issuer":155,"region":156,"url":657,"description":658,"useCases":78,"indexable":306},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":660,"label":661,"issuer":155,"region":156,"url":662,"description":663,"useCases":78,"indexable":306},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":665,"label":666,"issuer":667,"region":156,"url":668,"description":669,"useCases":443,"indexable":306},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":671,"label":672,"issuer":673,"region":162,"url":674,"description":675,"useCases":676,"indexable":306},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":678,"label":679,"issuer":155,"region":156,"url":680,"description":681,"useCases":676,"indexable":306},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":683,"label":684,"issuer":155,"region":156,"url":685,"description":686,"useCases":687,"indexable":306},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",6,{"id":689,"label":690,"issuer":691,"region":692,"url":693,"description":694,"useCases":77,"indexable":306},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":696,"label":697,"issuer":698,"region":156,"url":699,"description":700,"useCases":464,"indexable":306},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":702,"label":703,"issuer":704,"region":156,"url":705,"description":706,"useCases":464,"indexable":306},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":708,"label":709,"issuer":710,"region":566,"url":711,"description":712,"useCases":481,"indexable":306},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":714,"label":715,"issuer":155,"region":156,"url":716,"description":717,"useCases":481,"indexable":306},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":719,"label":720,"issuer":721,"region":162,"url":722,"description":723,"useCases":481,"indexable":306},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598306794]