[{"data":1,"prerenderedAt":648},["ShallowReactive",2],{"uc-public-consultation-response-analysis":3,"uc-regulations":441},{"useCase":4,"evidence":199,"blitsAiDeployments":333,"benchmarks":334,"indicative":359,"related":362,"indexability":439,"includeUnpublished":205},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":41,"valueDrivers":42,"kpis":47,"indicativeValue":54,"macroEstimates":83,"feasibility":84,"implementation":95,"risk":138,"blitsAi":177,"faq":179,"related":189,"datePublished":194,"dateModified":194,"lastVerified":194,"changelog":195,"slug":198},"AI for public consultation response analysis","Consultation response analysis","AI analysis of public consultation responses","AI proposes themes and maps every consultation response for human review. The UK Department for Transport reports over 92% raw agreement with human coders.","published","AI that reads every free text response to a public consultation or rulemaking comment period, proposes themes, maps each response to the themes that officials have validated, flags duplicates, campaign letters and responses that need special attention, and produces counts and summaries for the analysts who write the government's response.",[12,13,14,15],"consultation analysis AI","public comment analysis","AI thematic analysis of consultation responses","rulemaking comment review",[17],"government",[19,20],"citizen-services","analytics-and-reporting",[22,23,24],"summarization","classification-and-routing","content-generation",[26],"internal-tools","back-office","copilot","early-adopters","Governments ask the public for views before they change policy or make rules, and the answers\narrive as free text: a few hundred responses to a technical consultation, or tens of thousands\nwhen an issue catches public attention. Every response has to be read, coded against a set of\nthemes and counted, so that officials can show what people said and how it shaped the decision.\nDone by hand this can take months and, according to the UK Department for Transport, typically\nconsumes over half of the consultation budget; the UK government notes that the work is often\noutsourced to contractors.\n\nSpeed is not the only problem. Coding is subjective, so two analysts can put the same response\nunder different themes, and for very large consultations teams sometimes analyse a sample instead\nof every response. Mass campaigns and duplicate letters distort counts, and fake submissions have been\nused to manufacture the appearance of public support. Whatever tool is used, the government has\nto be able to show that every voice was heard and that the analysis was fair.",[32,37],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"Across the 500 consultations it runs each year, the UK government estimates that an AI tool could save officials around 75,000 days of analysis a year, work that costs GBP 20 million in staffing costs.","Government-built \"Humphrey\" AI tool reviews responses to consultation for first time, in bid to save millions","https://www.gov.uk/government/news/government-built-humphrey-ai-tool-reviews-responses-to-consultation-for-first-time-in-bid-to-save-millions",2025,{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":36},"The UK Department for Transport alone runs around 55 consultations a year, each generating free text that requires thematic analysis.","AI Consultation Analysis Tool v1.0 evaluation","https://assets.publishing.service.gov.uk/media/696f6641011505255b2d4203/ai-consultation-analysis-tool-CAT-evaluation.pdf","1. **Load and clean the responses.** Responses from the consultation platform, email and\n   regulations.gov style dockets are loaded per question, with personal data masked and exact and\n   near duplicates (campaign letters) grouped so they are counted but read once.\n2. **Propose themes.** A language model, or an ensemble of models, reads the responses to each\n   open question and proposes a set of themes, including rare but important points, with example\n   responses for each.\n3. **Validate the theme set with people.** Analysts read a random sample of responses, merge,\n   split, rename and add themes. Only the validated theme set is used from here on.\n4. **Map every response.** The model assigns each response to one or more validated themes and\n   records its stance (agree, disagree, neutral) where the question asks for one. Responses that\n   are off topic, abusive or that raise safeguarding concerns are flagged for a human.\n5. **Check and report.** Analysts review a sample of the mapping, correct errors and use the\n   counts, summaries and representative quotes (always verified against the original response)\n   to write the consultation response.",[43,44,45,46],"employee-productivity","speed","cost-to-serve","compliance",[48,49,50,51,52,53],"hours-saved","cost-reduction","cost-savings","accuracy","interactions-handled","processing-time-reduction",{"referenceOrg":55,"inputs":56,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A national ministry that runs 20 public consultations a year with substantial free text",[57,64,71],{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"consultations","Consultations with free text analysis per year",10,30,"consultations per year","Editorial assumption. The UK Department for Transport runs around 55 a year; replace with your own portfolio.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70,"sourceUrl":40},"costPerConsultation","Cost of analysing and reporting one medium sized consultation",50000,100000,"EUR per consultation","Editorial assumption, informed by the Department for Transport's estimate of GBP 80,000 to 100,000 for a medium sized consultation (about 15,000 responses). Replace with your own staff or contractor cost.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77,"sourceUrl":40},"savingShare","Share of that cost saved",0.3,0.5,"fraction of cost","Conservative against the Department for Transport's modelled estimate of 50 to 70 percent for a notional medium sized consultation, because theme review, synthesis and report writing remain human work and small consultations save less.","consultations * costPerConsultation * savingShare","EUR","per year","Consultation analysis cost avoided","Gross analysis cost avoided only. It leaves out the cost of running and assuring the tool, the value of faster policy decisions, and the option of analysing every response in consultations that are sampled today.",[],{"complexity":85,"complexityNote":86,"dataPrerequisites":87,"integrations":91},"low","The data is text the government already holds and no transaction systems are touched. The work is in method, not integration: a defensible human review step, an evaluation against human coded samples and a bias check across respondent groups.",[88,89,90],"Exported responses per question, with respondent type (individual, organisation) where collected","A few previously human coded consultations to evaluate against","A policy on how personal data in responses is masked and retained",[92,93,94],"Consultation platform or regulations.gov style docket export","Email inbox for responses submitted outside the platform","Analysis workspace or dashboard for analysts",{"steps":96,"guardrails":112,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":125},[97,100,103,106,109],{"title":98,"detail":99},"Evaluate on consultations you have already coded","Run the tool on two or three past consultations that humans coded, blind, and compare theme recall and mapping agreement with the human result before you use it live. Publish the method, as the UK Department for Transport did.",{"title":101,"detail":102},"Design the human theme review","Decide how many responses analysts read per question before they sign off the theme set, and write it down. The Department for Transport reports 1 to 5 hours per 100 responses for this step.",{"title":104,"detail":105},"Run the first live consultation in parallel","On the first live use, let analysts also code every response by hand, as the Scottish Government did with Consult, and measure where the two disagree.",{"title":107,"detail":108},"Handle campaigns and duplicates explicitly","Group identical and near identical responses, count them, and report organised campaigns separately so that one template letter does not read as thousands of independent views.",{"title":110,"detail":111},"Check for bias across respondent groups","Compare mapping accuracy for responses from different groups (for example by writing style, language or respondent type) and act on any gap before the method is used at scale.",[113,114,115,116,117],"Only human validated themes are used for mapping and counts","Every quote in the report is copied from the original response, never from a model summary","Personal data is masked before responses reach a model and in stored outputs","The tool never decides policy or weights responses; it organises them for analysts","Duplicate and campaign detection is reported, not used to discard responses","Analysts own the theme framework, review a random sample of the mapping for every question and write the response. Policy officials see the underlying responses behind every theme. Responses flagged for safeguarding or abuse go to a named person.",[120,121,122,123,124],"Theme recall and mapping agreement against a human coded sample, per question","Analyst hours per 1,000 responses, before and after","Days from consultation close to published response","Share of mapped responses changed by analysts during review","Accuracy differences across respondent groups",[126,129,132,135],{"title":127,"detail":128},"Missing the rare but important point","Models favour frequent themes and can miss a single expert response that changes the policy. Ask for rare themes explicitly and have analysts read a random sample.",{"title":130,"detail":131},"Hallucinated or softened quotes","A summary that paraphrases respondents can put words in their mouths. Pull quotes from the source text only.",{"title":133,"detail":134},"Counting as if consultations were polls","Consultation respondents are self selected; reporting that a percentage of respondents felt something invites misreading. Report counts with context, as the Department for Transport cautions.",{"title":136,"detail":137},"Campaigns and fake submissions distorting results","Mass template letters or fabricated submissions can swamp genuine views. Detect duplicates and unusual submission patterns and report them openly.",{"euAiAct":139,"regulations":142,"guidance":149,"controls":166,"incidents":172},{"tier":140,"basis":141},"minimal","Organising and summarising consultation responses for analysts does not decide on individuals and is not listed in Annex III, so no high risk obligations apply. If AI generated text is published to inform the public on matters of public interest without human review and editorial responsibility, Article 50(4) requires disclosure.",[143,144,145,146,147,148],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","uk-gdpr","uk-atrs",[150,156,161],{"title":151,"issuer":152,"region":153,"url":154,"note":155},"Algorithmic Transparency Recording Standard hub","Government Digital Service","europe","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","The standard is mandatory for all UK government departments; the Department for Science, Innovation and Technology has published a record for Consult describing its purpose, data, human oversight and risks.",{"title":157,"issuer":158,"region":153,"url":159,"note":160},"DSIT: Consult (algorithmic transparency record)","Department for Science, Innovation and Technology","https://www.gov.uk/algorithmic-transparency-records/dsit-consult","The published transparency record for the UK government's Consult tool, an example of what to disclose about a consultation analysis tool.",{"title":162,"issuer":163,"region":153,"url":164,"note":165},"Public attitudes to the use of AI in DfT consultations and correspondence","Department for Transport","https://www.gov.uk/government/publications/public-attitudes-to-the-use-of-ai-in-dft-consultations-and-correspondence","Research on what the public expects before AI is used to analyse their consultation responses; it shaped the design of the Department for Transport's tool.",[167,168,169,170,171],"Published method and evaluation before live use, including a transparency record","Documented human theme review with a sample size rule","Audit trail from each reported count back to the underlying responses","Bias testing across respondent groups on every major model or prompt change","Retention and masking rules for personal data in responses",[173],{"title":174,"url":175,"note":176},"New York Attorney General: millions of fake comments in the FCC's 2017 net neutrality proceeding","https://ag.ny.gov/press-release/2021/attorney-general-james-issues-report-detailing-millions-fake-comments-revealing","The investigation found that nearly 18 million of the more than 22 million comments were fake, including 9.3 million using fictitious identities, most of them submitted by one person using automated software. Comment analysis must detect campaigns and fabricated submissions rather than count them as public opinion.",{"howToBuild":178},"On Blits.ai this is an **agentic workflow** that runs over the exported responses. Responses are\nuploaded as CSV or spreadsheet files into the **document library**, **PII masking** at the\ngateway masks personal data, and an **agent** with **structured output** proposes themes per\nquestion. A **human in the loop** confirmation step lets an analyst approve or reject the\nproposed theme set before the workflow maps every response to the approved themes. Loaded into a database registered as a\n**SQL knowledge base**, the results can be queried by an agent when analysts ask about counts\nand themes.\n\n**Test suites** run previously human coded responses against the workflow and grade its theme\nassignments, so every prompt or model change is checked before live use. Every run keeps a full\naudit trail and downloadable run data. The platform is **model agnostic** and offers EU and UAE\ndata residency, so a ministry can keep responses in region and choose the models it uses.",[180,183,186],{"question":181,"answer":182},"How accurate is AI at coding consultation responses?","Close to human coders when humans validate the themes. The UK Department for Transport reports over 92% raw agreement between its tool and human coders (an F1 score of 0.75 for theme mapping in its blind evaluation), and the UK government reported an F1 score of 0.76 on Consult's first live consultation. Human review of the theme set remains necessary; without it, the Department for Transport's tool found about 75% of the human themes.",{"question":184,"answer":185},"Can AI replace the analysts?","No. It replaces most of the reading and tagging, while analysts decide the themes, check the mapping and write the response. The Department for Transport estimates, in a model rather than a measured result, savings of 50 to 70% of the cost of a medium sized consultation, with review, synthesis and report writing remaining human work.",{"question":187,"answer":188},"Should respondents be told that AI analyses their responses?","Yes. Say so in the consultation document and privacy notice, publish the method and keep a human accountable for the analysis. In the UK the Algorithmic Transparency Recording Standard is mandatory for government departments, and the Department for Science, Innovation and Technology has published a record for Consult.",[190,191,192,193],"customer-feedback-analysis","civil-servant-drafting-copilot","complaints-root-cause-analysis","freedom-of-information-request-processing","2026-09-27",[196],{"date":194,"note":197},"First published","public-consultation-response-analysis",[200,238,282,302,317],{"title":201,"useCases":202,"organization":203,"vendors":207,"summary":211,"stage":212,"year":36,"channels":213,"languages":214,"metrics":216,"outcomeDisclosed":224,"sources":225,"verification":233,"grade":235,"id":236,"organizationSlug":237},"UK government i.AI: Consult for public consultation analysis, first used live by the Scottish Government",[198],{"name":204,"anonymized":205,"country":206,"region":153,"industry":17},"Department for Science, Innovation and Technology (Incubator for Artificial Intelligence)",false,"GB",[208],{"name":209,"role":210},"Incubator for Artificial Intelligence (i.AI)","in-house","Consult is a generative AI tool built by the UK government's Incubator for Artificial Intelligence as part of the Humphrey suite. It proposes themes for each open question of a consultation, maps every response to those themes and shows the result in a dashboard that officials review and correct. Its first live use was on a Scottish Government consultation about non surgical cosmetic procedures, where it analysed more than 2,000 responses while officials also reviewed every response by hand; the government reported an F1 score of 0.76 in this first live evaluation. In July 2026 access was managed through a waitlist ahead of a cross government rollout planned for 2027.","pilot",[26],[215],"en",[217],{"kpi":52,"value":218,"unit":219,"qualifier":220,"period":221,"claimant":222,"quote":223,"sourceUrl":35},2000,"count","at-least","first live consultation (Scottish Government, six open questions)","organization","Reviewing comments from over 2,000 consultation responses using generative AI, Consult identified key themes that feedback fell into across each of six qualitative questions.",true,[226,228,229],{"url":35,"title":34,"publisher":158,"date":227},"2025-05-14",{"url":159,"title":157,"publisher":158,"date":227},{"url":230,"title":231,"publisher":158,"date":232},"https://www.gov.uk/government/publications/consult-register-your-interest/consult-ai-tool","Consult: AI tool","2026-07-22",{"level":234,"checkedAt":194},"source-verified","B","uk-incubator-for-ai-consult-consultation-analysis",null,{"title":239,"useCases":240,"organization":241,"vendors":242,"summary":248,"stage":212,"year":36,"channels":249,"languages":250,"metrics":251,"outcomeDisclosed":224,"sources":273,"verification":280,"grade":235,"id":281,"organizationSlug":237},"UK Department for Transport: Consultation Analysis Tool, evaluated with The Alan Turing Institute",[198],{"name":163,"anonymized":205,"country":206,"region":153,"industry":17},[243,245],{"name":244,"role":210},"Department for Transport AI and Data Science team",{"name":246,"role":247},"The Alan Turing Institute","integrator","The Department for Transport runs around 55 consultations a year and co developed a Consultation Analysis Tool (CAT) with The Alan Turing Institute. An ensemble of large language models proposes themes and rare \"golden insights\", humans validate the themes in a structured review of a random sample, and the models then map every response to the validated themes. The published evaluation compares the tool with human coded datasets in blind and live settings, tests for accuracy differences across demographic groups (no evidence of systematic bias on the three questions analysed; small differences by ethnicity, under 3 percentage points and favouring minority groups, were of low practical significance) and estimates that the tool would save around 50 to 70 percent of the cost and time of a notional medium sized consultation (a modelled estimate, not a measured result).",[26],[215],[252,257,263,268],{"kpi":52,"value":253,"unit":219,"qualifier":254,"period":255,"claimant":222,"quote":256,"sourceUrl":40},200000,"approximately","live pilots to date (December 2025 report)","The CAT has now been piloted on multiple live consultations, analysing 200,000 responses (exceeding 8 million words) to date.",{"kpi":48,"value":258,"unit":259,"qualifier":254,"period":260,"baseline":261,"claimant":222,"quote":262,"sourceUrl":40},15000,"hours","cumulative over four consultation and call for evidence or ideas projects to date (December 2025 report), modelled counterfactual","Modelled scenario in which all responses are analysed manually by humans","even with this investment, the CAT has roughly saved 15,000 hours of work to date compared to a scenario where all responses for all consultations are rigorously analysed by humans manually.",{"kpi":50,"value":264,"unit":265,"currency":266,"qualifier":254,"period":260,"baseline":261,"claimant":222,"quote":267,"sourceUrl":40},500000,"currency","GBP","the CAT has analysed 200,000 responses and over 8 million words, saving an estimated £0.5 million compared to a scenario where all responses for all consultations were rigorously analysed by humans.",{"kpi":51,"value":269,"unit":270,"qualifier":220,"period":271,"claimant":222,"quote":272,"sourceUrl":40},92,"percent","theme mapping, raw agreement with human coders in blind and live evaluations","The CAT-vs-human inter-rater reliability (IRR), using metrics commonly employed in qualitative research to assess how consistently two or more researchers analyse the same data, achieved over 92% overall raw agreement in both our blind and non-blind evaluation designs.",[274,278],{"url":275,"title":276,"publisher":163,"date":277},"https://www.gov.uk/government/publications/ai-consultation-analysis-tool-evaluation","AI Consultation Analysis Tool evaluation","2025-12-23",{"url":40,"title":279,"publisher":163},"AI Consultation Analysis Tool v1.0 evaluation (PDF)",{"level":234,"checkedAt":194},"uk-department-for-transport-consultation-analysis-tool",{"title":283,"useCases":284,"organization":285,"vendors":289,"summary":290,"stage":291,"year":36,"channels":292,"languages":293,"metrics":294,"outcomeDisclosed":205,"sources":295,"verification":300,"grade":235,"id":301,"organizationSlug":237},"US Department of Transportation: Public Comment Analyzer for regulations.gov dockets",[198],{"name":286,"anonymized":205,"country":287,"region":288,"industry":17},"U.S. Department of Transportation, Office of the Secretary","US","north-america",[],"The Office of the Secretary of Transportation reports a Public Comment Analyzer, deployed in February 2025, that uses a language model to categorise public comments by topic, detect their sentiment, generate summaries and provide daily updates on the comments to subject matter experts. The stated aim is to reduce the human effort of reading every comment and to let experts go from summaries to the underlying comments where needed. No measured outcome is published.","production",[26],[215],[],[296],{"url":297,"title":298,"publisher":299},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 federal agency AI use case inventory, individually reported use cases (raw data)","Office of Management and Budget (GitHub)",{"level":234,"checkedAt":194},"us-department-of-transportation-public-comment-analyzer",{"title":303,"useCases":304,"organization":305,"vendors":307,"summary":308,"stage":291,"year":309,"channels":310,"languages":311,"metrics":312,"outcomeDisclosed":205,"sources":313,"verification":315,"grade":235,"id":316,"organizationSlug":237},"US Centers for Disease Control and Prevention: generative AI stance analysis of public comments on proposed rules",[198],{"name":306,"anonymized":205,"country":287,"region":288,"industry":17},"Centers for Disease Control and Prevention",[],"CDC reports in the 2025 federal AI use case inventory that it uses generative AI to analyse public comments on its proposed rules. For each comment the system records a stance (support, oppose or neutral), topics and sentiment, which regulatory analysts use when they review and summarise the feedback for the rulemaking record. The inventory lists the use case as deployed since July 2023. No outcome figures are published.",2023,[26],[215],[],[314],{"url":297,"title":298,"publisher":299},{"level":234,"checkedAt":194},"cdc-public-comment-stance-analysis",{"title":318,"useCases":319,"organization":320,"vendors":322,"summary":323,"stage":291,"year":324,"channels":325,"languages":326,"metrics":327,"outcomeDisclosed":205,"sources":328,"verification":330,"grade":235,"id":331,"organizationSlug":332},"Federal Reserve Board: Comment Review System for public comments on proposed rules",[198],{"name":321,"anonymized":205,"country":287,"region":288,"industry":17},"Board of Governors of the Federal Reserve System",[],"The Federal Reserve Board processes public comments on rulemakings, information collections and other proposals in its Comment Review System. The system uses traditional natural language processing for summaries, matching comments to lists of topics, entity identification and similarity matching, and flags duplicate and near duplicate comment letters. The Board states that all public comments are still reviewed in their entirety and that summaries only assist the review. The inventory lists it as deployed since July 2021; no outcome figures are published.",2021,[26],[215],[],[329],{"url":297,"title":298,"publisher":299},{"level":234,"checkedAt":194},"federal-reserve-board-public-comment-review-system","board-of-governors-of-the-federal-reserve-system",0,[335,343,349,354],{"kpi":52,"label":336,"unit":219,"aggregate":205,"higherIsBetter":224,"n":337,"nUpTo":333,"median":338,"min":218,"max":253,"byClaimant":339,"vendorOnly":205,"points":340},"Interactions handled",2,101000,{"organization":337,"vendor":333,"regulator":333,"independent":333},[341,342],{"evidenceId":281,"organization":163,"value":253,"qualifier":254,"claimant":222,"grade":235,"pooled":224},{"evidenceId":236,"organization":204,"value":218,"qualifier":220,"claimant":222,"grade":235,"pooled":224},{"kpi":51,"label":344,"unit":270,"aggregate":224,"higherIsBetter":224,"n":345,"nUpTo":333,"median":269,"min":269,"max":269,"byClaimant":346,"vendorOnly":205,"points":347},"Accuracy",1,{"organization":345,"vendor":333,"regulator":333,"independent":333},[348],{"evidenceId":281,"organization":163,"value":269,"qualifier":220,"claimant":222,"grade":235,"pooled":224},{"kpi":50,"label":350,"unit":265,"currency":266,"aggregate":205,"higherIsBetter":224,"n":345,"nUpTo":333,"median":264,"min":264,"max":264,"byClaimant":351,"vendorOnly":205,"points":352},"Cost savings",{"organization":345,"vendor":333,"regulator":333,"independent":333},[353],{"evidenceId":281,"organization":163,"value":264,"qualifier":254,"claimant":222,"grade":235,"pooled":224},{"kpi":48,"label":355,"unit":259,"aggregate":205,"higherIsBetter":224,"n":345,"nUpTo":333,"median":258,"min":258,"max":258,"byClaimant":356,"vendorOnly":205,"points":357},"Hours saved",{"organization":345,"vendor":333,"regulator":333,"independent":333},[358],{"evidenceId":281,"organization":163,"value":258,"qualifier":254,"claimant":222,"grade":235,"pooled":224},{"low":360,"high":361},150000,1500000,[363,389,405,423],{"slug":190,"title":364,"shortTitle":365,"definition":366,"status":9,"industries":367,"functions":371,"patterns":374,"audience":27,"autonomy":28,"adoptionStage":376,"evidenceCount":377,"publicEvidenceCount":377,"organizations":378,"bestGrade":235,"headline":384,"lastVerified":194,"indexable":224},"AI for voice of the customer and feedback analysis","Customer feedback analysis","AI that reads every piece of free text customer feedback, such as survey verbatims, NPS comments, reviews, social posts, chat and call transcripts, and turns it into themes, sentiment, drivers and suggested actions that a named owner can act on, so the organization hears all of its customers instead of a sample.",[368,369,17,370],"cross-industry","retail-and-ecommerce","manufacturing",[372,373,20],"customer-service","marketing",[23,22,375],"speech-analytics","mainstream",5,[379,380,381,382,383],"U.S. Department of Housing and Urban Development","Majid Al Futtaim Retail","Mattel","SBF Group","U.S. Social Security Administration",{"kpi":51,"label":344,"unit":270,"n":345,"nUpTo":333,"kind":385,"value":386,"qualifier":387,"claimant":388,"organization":382,"vendorReported":224},"reported",84,"exact","vendor",{"slug":191,"title":390,"shortTitle":391,"definition":392,"status":9,"industries":393,"functions":394,"patterns":397,"audience":399,"autonomy":28,"adoptionStage":400,"evidenceCount":377,"publicEvidenceCount":377,"organizations":401,"bestGrade":235,"headline":237,"lastVerified":194,"indexable":224},"AI drafting copilot for civil servants for correspondence, briefings and ministerial replies","Civil servant drafting copilot","A generative AI assistant that drafts replies to correspondence from the public and elected representatives, briefings, submissions and summaries for civil servants, grounded in the department's approved lines, policy documents and case data, with the official editing and approving every word before it is sent or cleared.",[17],[19,395,396],"knowledge-management","case-management",[24,398,22],"rag-knowledge-assistant","employee-facing","emerging",[402,403,404,204,152],"Cabinet Office (Government Communication Service)","Crown Prosecution Service","Department for Education",{"slug":192,"title":406,"shortTitle":407,"definition":408,"status":9,"industries":409,"functions":414,"patterns":416,"audience":27,"autonomy":28,"adoptionStage":400,"segment":418,"evidenceCount":419,"publicEvidenceCount":419,"organizations":420,"bestGrade":235,"headline":237,"lastVerified":194,"indexable":224},"AI for complaints root cause and systemic issue analysis","Complaints root cause analysis","AI that reads the free text of complaints across all channels, clusters them into themes, separates systemic causes from one off events, links each theme to the product, process or control behind it and routes the insight to the owner who can fix it, with a human validating every root cause and every remediation.",[368,410,411,412,413,17],"banking","insurance","payments","telecommunications",[415,372,20],"regulatory-compliance",[23,22,417,398],"agentic-workflow","second-line",3,[421,321,422],"Centers for Medicare and Medicaid Services","Federal Trade Commission",{"slug":193,"title":424,"shortTitle":425,"definition":426,"status":9,"industries":427,"functions":428,"patterns":430,"audience":399,"autonomy":28,"adoptionStage":29,"evidenceCount":432,"publicEvidenceCount":432,"organizations":433,"bestGrade":235,"headline":237,"lastVerified":438,"indexable":224},"AI for freedom of information request processing","Freedom of information requests","AI that helps a public body handle freedom of information and open government requests: logging and clarifying requests, spotting duplicates, searching and deduplicating the records in scope, proposing redactions with the exemption that applies, and drafting the response letter, with an FOI officer deciding what is released.",[17],[19,429,396],"legal",[431,23,24],"document-processing",4,[434,435,436,437],"U.S. Department of Justice","U.S. Food and Drug Administration, Center for Drug Evaluation and Research","Provincie Noord-Holland","U.S. Department of the Interior","2026-09-26",{"indexable":224,"reasons":440},[],[442,448,453,460,466,472,478,485,493,500,507,513,518,525,531,536,543,549,555,561,567,573,578,583,588,595,602,607,613,620,626,632,638,643],{"id":143,"label":443,"issuer":444,"region":153,"url":445,"description":446,"useCases":447,"indexable":224},"EU AI Act","European Union","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":144,"label":449,"issuer":444,"region":153,"url":450,"description":451,"useCases":452,"indexable":224},"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":146,"label":454,"issuer":455,"region":456,"url":457,"description":458,"useCases":459,"indexable":224},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":145,"label":461,"issuer":462,"region":288,"url":463,"description":464,"useCases":465,"indexable":224},"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":467,"label":468,"issuer":444,"region":153,"url":469,"description":470,"useCases":471,"indexable":224},"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":147,"label":473,"issuer":474,"region":153,"url":475,"description":476,"useCases":477,"indexable":224},"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":479,"label":480,"issuer":481,"region":153,"url":482,"description":483,"useCases":484,"indexable":224},"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":486,"label":487,"issuer":488,"region":489,"url":490,"description":491,"useCases":492,"indexable":224},"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":494,"label":495,"issuer":496,"region":489,"url":497,"description":498,"useCases":499,"indexable":224},"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":501,"label":502,"issuer":503,"region":456,"url":504,"description":505,"useCases":506,"indexable":224},"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":508,"label":509,"issuer":510,"region":288,"url":511,"description":512,"useCases":506,"indexable":224},"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":148,"label":514,"issuer":515,"region":153,"url":154,"description":516,"useCases":517,"indexable":224},"UK Algorithmic Transparency Recording Standard","UK Government","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":519,"label":520,"issuer":521,"region":456,"url":522,"description":523,"useCases":524,"indexable":224},"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":526,"label":527,"issuer":444,"region":153,"url":528,"description":529,"useCases":530,"indexable":224},"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":532,"label":533,"issuer":444,"region":153,"url":534,"description":535,"useCases":530,"indexable":224},"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":537,"label":538,"issuer":539,"region":288,"url":540,"description":541,"useCases":542,"indexable":224},"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":544,"label":545,"issuer":444,"region":153,"url":546,"description":547,"useCases":548,"indexable":224},"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.",12,{"id":550,"label":551,"issuer":552,"region":288,"url":553,"description":554,"useCases":548,"indexable":224},"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":556,"label":557,"issuer":558,"region":456,"url":559,"description":560,"useCases":548,"indexable":224},"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":562,"label":563,"issuer":444,"region":153,"url":564,"description":565,"useCases":566,"indexable":224},"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.",11,{"id":568,"label":569,"issuer":570,"region":288,"url":571,"description":572,"useCases":566,"indexable":224},"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":574,"label":575,"issuer":488,"region":489,"url":576,"description":577,"useCases":60,"indexable":224},"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":579,"label":580,"issuer":444,"region":153,"url":581,"description":582,"useCases":60,"indexable":224},"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":584,"label":585,"issuer":444,"region":153,"url":586,"description":587,"useCases":60,"indexable":224},"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":589,"label":590,"issuer":591,"region":153,"url":592,"description":593,"useCases":594,"indexable":224},"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.",9,{"id":596,"label":597,"issuer":598,"region":288,"url":599,"description":600,"useCases":601,"indexable":224},"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":603,"label":604,"issuer":444,"region":153,"url":605,"description":606,"useCases":601,"indexable":224},"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":608,"label":609,"issuer":444,"region":153,"url":610,"description":611,"useCases":612,"indexable":224},"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":614,"label":615,"issuer":616,"region":617,"url":618,"description":619,"useCases":377,"indexable":224},"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":621,"label":622,"issuer":623,"region":153,"url":624,"description":625,"useCases":432,"indexable":224},"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":627,"label":628,"issuer":629,"region":153,"url":630,"description":631,"useCases":432,"indexable":224},"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":633,"label":634,"issuer":635,"region":489,"url":636,"description":637,"useCases":419,"indexable":224},"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":639,"label":640,"issuer":444,"region":153,"url":641,"description":642,"useCases":419,"indexable":224},"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":644,"label":645,"issuer":422,"region":288,"url":646,"description":647,"useCases":419,"indexable":224},"us-fcra","Fair Credit Reporting Act","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.",1790598301173]