{"id":165,"date":"2026-10-13T12:32:08","date_gmt":"2026-10-13T12:32:08","guid":{"rendered":"https:\/\/therisk.global\/nexus-foundry\/?page_id=165"},"modified":"2026-10-13T23:16:04","modified_gmt":"2026-10-13T23:16:04","slug":"ai-ml","status":"publish","type":"page","link":"https:\/\/therisk.global\/nexus-foundry\/stem\/ai-ml\/","title":{"rendered":"AI and machine learning at Nexus Foundry"},"content":{"rendered":"\n<!-- Nexus network page \u00b7 https:\/\/therisk.global\/nexus-foundry\/stem\/ai-ml\/ \u00b7 source template \u00b7 generated by integrations\/wordpress\/network\/build.mjs, never edit this file --><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-fig-engine\" 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auto;padding:0 16px;font-family:ui-sans-serif,system-ui,-apple-system,\"Segoe UI\",sans-serif;line-height:1.55;overflow-wrap:break-word}#nxe-nsrzgr0x.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-nsrzgr0x.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-nsrzgr0x.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-nsrzgr0x.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-nsrzgr0x.nxe :where(ul,ol){padding-left:20px}#nxe-nsrzgr0x.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-nsrzgr0x.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-nsrzgr0x.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-nsrzgr0x.nxe svg{display:none}#nxe-nsrzgr0x.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-nsrzgr0x.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-nsrzgr0x.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"protocol\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>G09 \u00b7 METHOD \u00b7 ai-ml<\/strong>GCRI dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GCRI<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Nexus Foundry<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Established<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Method page \u00b7 Artificial intelligence and machine learning<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">AI and machine learning at Nexus Foundry<\/h1><p class=\"nx-tagline\">Artificial intelligence and machine learning extend detection, classification and forecasting across large archives, and agents extend them into action. In this field the method is also a source of risk: models that fail when conditions shift, outputs that sound certain when they are not, and tools that turn an error into an effect.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/nexus-foundry\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/nexus-foundry\/stem\/\">Back to STEM foundation of Nexus Foundry<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-lv-steps\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Protocol<\/strong><\/span><span class=\"nx-eyebrow\">6 steps<\/span><\/div><ol><li><a href=\"#nx-sec-brief-answer\"><span class=\"nx-lv-steps-n\">01<\/span><span class=\"nx-lv-steps-t\">In brief<\/span><span class=\"nx-lv-steps-d\">The short answer<\/span><\/a><\/li><li><a href=\"#nx-sec-application\"><span class=\"nx-lv-steps-n\">02<\/span><span class=\"nx-lv-steps-t\">Application<\/span><span class=\"nx-lv-steps-d\">What artificial intelligence and machine learning does here<\/span><\/a><\/li><li><a href=\"#nx-sec-terms\"><span class=\"nx-lv-steps-n\">03<\/span><span class=\"nx-lv-steps-t\">Key terms<\/span><span class=\"nx-lv-steps-d\">Terms used on this page<\/span><\/a><\/li><li><a href=\"#nx-sec-canvas\"><span class=\"nx-lv-steps-n\">04<\/span><span class=\"nx-lv-steps-t\">Canvas<\/span><span class=\"nx-lv-steps-d\">The method as a run graph<\/span><\/a><\/li><li><a href=\"#nx-sec-data\"><span class=\"nx-lv-steps-n\">05<\/span><span class=\"nx-lv-steps-t\">Data<\/span><span class=\"nx-lv-steps-d\">What the method consumes<\/span><\/a><\/li><li><a href=\"#nx-sec-checks\"><span class=\"nx-lv-steps-n\">06<\/span><span class=\"nx-lv-steps-t\">Validation<\/span><span class=\"nx-lv-steps-d\">How results are checked<\/span><\/a><\/li><\/ol><p class=\"nx-register-foot\">GCRI \u00b7 Established \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-s-3-sec\"><span>02<\/span>Model training, in outline<\/a><a href=\"#nx-sec-application\"><span>03<\/span>What artificial intelligence and\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-field\"><span>05<\/span>Digital demand and connectivity in\u2026<\/a><a href=\"#nx-sec-canvas\"><span>06<\/span>The method as a run graph<\/a><a href=\"#nx-sec-data\"><span>07<\/span>What the method consumes<\/a><a href=\"#nx-sec-checks\"><span>08<\/span>How results are checked<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"infrastructure\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Machine learning model governance is the set of practices that decide which models may be used, for what and on what evidence. It covers validation designed for the data, such as spatial cross-validation for earth observation, model cards and promotion records, red-team findings and agents limited to declared scopes with receipts for their actions.<\/p><\/div><\/div><\/section><section id=\"nx-s-3-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Console<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Model training, in outline.<\/h2><p class=\"nx-lede\">The working screen behind this method: its structure, terms and states. Names are placeholders and values illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-foundry-method-ml\" data-cx=\"foundry.method-ml\" data-variant=\"section\" aria-label=\"Model training: illustrative console with placeholder names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Model training<\/h3><p class=\"nx-fk-sub\">Foundry \u00b7 AI and machine learning at Nexus\u2026 \u00b7 Run R-6794<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure's values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Drift on one slice<\/span><span class=\"nx-cx-prod\">GCRI \u00b7 Nexus Foundry<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"4\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Evaluation by slice<\/h4><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-foundry-method-ml-t0\"><table class=\"nx-fk-table\" data-fk-table=\"cx-foundry-method-ml-t0\" aria-label=\"Evaluation by slice\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Slice<\/button><\/th><th scope=\"col\" data-col=\"c1\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">AUC<\/button><\/th><th scope=\"col\" data-col=\"c2\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Calibration error<\/button><\/th><th scope=\"col\" data-col=\"c3\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Samples<\/button><\/th><th scope=\"col\" data-col=\"c4\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c4\">State<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-foundry-method-ml:r0-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>All<\/td><td data-num=\"true\" data-sv=\"0.83\">0.83<\/td><td data-num=\"true\" data-sv=\"0.02\">0.02<\/td><td data-num=\"true\" data-sv=\"5712\">5712<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Pass<\/span><\/td><\/tr><tr data-fk-key=\"cx-foundry-method-ml:r0-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Region A<\/td><td data-num=\"true\" data-sv=\"0.74\">0.74<\/td><td data-num=\"true\" data-sv=\"0.05\">0.05<\/td><td data-num=\"true\" data-sv=\"1098\">1098<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Review<\/span><\/td><\/tr><tr data-fk-key=\"cx-foundry-method-ml:r0-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\" aria-current=\"true\" data-cur=\"true\"><td>Region B<\/td><td data-num=\"true\" data-sv=\"0.76\">0.76<\/td><td data-num=\"true\" data-sv=\"0.05\">0.05<\/td><td data-num=\"true\" data-sv=\"458\">458<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Review<\/span><\/td><\/tr><tr data-fk-key=\"cx-foundry-method-ml:r0-3\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Rare class<\/td><td data-num=\"true\" data-sv=\"0.64\">0.64<\/td><td data-num=\"true\" data-sv=\"0.07\">0.07<\/td><td data-num=\"true\" data-sv=\"160\">160<\/td><td><span class=\"nx-fk-pill\" data-state=\"not\"><i aria-hidden=\"true\"><\/i>Below bar<\/span><\/td><\/tr><\/tbody><\/table><\/div><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"2\" data-t=\"kv\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Method card<\/h4><\/div><\/div><dl class=\"nx-fk-dl\"><div><dt>Method<\/dt><dd>Artificial intelligence and\u2026<\/dd><\/div><div><dt>Split<\/dt><dd>time-based, no leakage<\/dd><\/div><div><dt>Features<\/dt><dd>22<\/dd><\/div><div><dt>Label<\/dt><dd><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>MODELLED<\/span><\/dd><\/div><\/dl><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Population stability<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Population stability\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-foundry-method-ml-c2\" data-fk-xs=\"lin,0,23,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-series=\"Drift index|ink|l||0:46.6;1:44;2:43.9;3:46.6;4:51.5;5:52.5;6:49.9;7:55.9;8:56.5;9:59.6;10:64.5;11:70.7;12:66.4;13:72.1;14:72.1;15:72.1;16:73.4;17:77.1;18:78.8;19:81.8;20:87.4;21:91.2;22:88.2;23:89.9\" data-nx-z=\"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\"><\/svg><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Model training: the screen's structure, terms and states for Foundry.<\/span><span data-honesty=\"true\">Placeholder names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"infrastructure\" data-variant=\"split\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">What artificial intelligence and machine learning does here.<\/h2><p class=\"nx-lede\">Artificial intelligence and machine learning develop algorithms that learn patterns from data to classify, predict or generate outputs. They are increasingly used for hazard detection, forecasting and decision support, and are themselves a source of risk. In data and compute and methods and modelling, machine learning can scale detection and forecasting across interconnected systems. Its own failure modes must be weighed when automated outputs inform consequential decisions. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M13 7 9 3 5 7l4 4\"\/><path d=\"m17 11 4 4-4 4-4-4\"\/><path d=\"m8 12 4 4 6-6-4-4Z\"\/><path d=\"m16 8 3-3\"\/><path d=\"M9 21a6 6 0 0 0-6-6\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Earth observation models with spatial validation<\/h3><p class=\"nx-p nx-mt-1\">Land cover, building and damage models are trained and tested on spatially separated blocks, so reported accuracy reflects new places rather than the neighbours of training pixels.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><rect width=\"8\" height=\"4\" x=\"8\" y=\"2\" rx=\"1\" ry=\"1\"\/><path d=\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2\"\/><path d=\"m9 14 2 2 4-4\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Model cards and promotion records<\/h3><p class=\"nx-p nx-mt-1\">Each model version carries its intended use, training data, evaluation by region and subgroup, known limits and red-team results, and is promoted only with a recorded review.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M12 5a3 3 0 1 0-5.997.125 4 4 0 0 0-2.526 5.77 4 4 0 0 0 .556 6.588A4 4 0 1 0 12 18Z\"\/><path d=\"M9 13a4.5 4.5 0 0 0 3-4\"\/><path d=\"M6.003 5.125A3 3 0 0 0 6.401 6.5\"\/><path d=\"M3.477 10.896a4 4 0 0 1 .585-.396\"\/><path d=\"M6 18a4 4 0 0 1-1.967-.516\"\/><path d=\"M12 13h4\"\/><path d=\"M12 18h6a2 2 0 0 1 2 2v1\"\/><path d=\"M12 8h8\"\/><path d=\"M16 8V5a2 2 0 0 1 2-2\"\/><circle cx=\"16\" cy=\"13\" r=\".5\"\/><circle cx=\"18\" cy=\"3\" r=\".5\"\/><circle cx=\"20\" cy=\"21\" r=\".5\"\/><circle cx=\"20\" cy=\"8\" r=\".5\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Scoped agents with receipts<\/h3><p class=\"nx-p nx-mt-1\">Agents run analyses with a declared tool set, data classes, budget and kill-switch enforced at the policy gate, and every action produces a receipt for a reviewer.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Spatial cross-validation<\/td><td data-label=\"Definition\">Validation that holds out whole regions, so a model is tested on places it has not seen.<\/td><\/tr><tr><td data-label=\"Term\">Model promotion<\/td><td data-label=\"Definition\">The recorded decision to move a model from testing into use, with its evidence.<\/td><\/tr><tr><td data-label=\"Term\">Out-of-distribution test set<\/td><td data-label=\"Definition\">Data deliberately unlike the training data, used to see how a model fails.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"infrastructure\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Geography<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">Digital demand and connectivity in Middle East and North Africa.<\/h2><p class=\"nx-lede\">Urban demand centres and the hubs that anchor digital and data infrastructure, shown for Middle East and North Africa as an example geography for this subject.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-map nx-cmap\" data-nx-map=\"ctx\" data-cmap=\"middle-east:digital\" data-ramp=\"purple\" data-basis=\"case\" data-focus=\"0 0 2000 1000|332 295 755 726|1248 419 1719 789|972 670 1359 1000|706 404 1045 747\"><figure class=\"nx-fk\" data-fk=\"map-middle-east-digital\" data-ramp=\"purple\" 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\"><figcaption class=\"nx-fk-cap\"><span>Urban demand centres and the hubs that anchor digital and data infrastructure.<\/span><span data-honesty=\"true\">Indicator derived from Natural Earth at map scale; a screening view, not an assessment.<\/span><\/figcaption><\/figure><div class=\"nx-live-slot nx-live-slot--map\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gcri\/G09\/stem\/ai-ml\/#map\" data-nxf-title=\"Nexus Foundry live map\" data-nxf-height=\"560\" hidden><\/div><div class=\"nx-map-hud\"><span class=\"nx-eyebrow\">GCRI \u00b7 Nexus Foundry \u00b7 field<\/span><span class=\"nx-eyebrow nx-map-hud-right\">DESIGN FIGURE<\/span><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-canvas\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">The method as a run graph.<\/h2><p class=\"nx-lede\">Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Method canvas<\/strong> \u00b7 Artificial intelligence and machine learning as applied here<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-g09-stem-ai-ml-ai-t nx-dag-g09-stem-ai-ml-ai-d\" data-nx-z=\"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\"><title id=\"nx-dag-g09-stem-ai-ml-ai-t\">Artificial intelligence and machine learning in Nexus Foundry: AI risk management, govern to manage<\/title><desc id=\"nx-dag-g09-stem-ai-ml-ai-d\">Governance sets roles and policies; the context and intended use are mapped; capability, safety, robustness and fairness are measured; risks are managed with mitigations and a deployment gate; incidents feed monitoring and the registry. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 01<\/strong><span>Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"infrastructure\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Data<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by artificial intelligence and machine learning on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-foundry\/research\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">Labelled training sets<\/td><td data-label=\"Role\">Supervised learning<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Satellite image archives<\/td><td data-label=\"Role\">Inputs to classification<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Held-out and out-of-distribution test sets<\/td><td data-label=\"Role\">Evaluation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Evaluation and red-team records<\/td><td data-label=\"Role\">Promotion decisions<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Datasheets for training data<\/td><td data-label=\"Role\">Origin, consent and licence<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gcri\/G09\/stem\/ai-ml\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G09\/stem\/ai-ml\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Validation<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Spatial and temporal hold-out<\/td><td data-label=\"How it is done\">Test data are separated in space and time from training data to prevent leakage<\/td><\/tr><tr><td data-label=\"Check\">Probability calibration<\/td><td data-label=\"How it is done\">Predicted probabilities are compared with observed frequencies on held-out data<\/td><\/tr><tr><td data-label=\"Check\">Drift monitoring<\/td><td data-label=\"How it is done\">Input and output distributions in use are compared with those at validation, and divergence triggers review<\/td><\/tr><tr><td data-label=\"Check\">Adversarial testing<\/td><td data-label=\"How it is done\">Models and agents are tested against prompt injection and misuse before promotion<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.<\/p><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section\" data-hue=\"infrastructure\" data-variant=\"stack\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-foundry\/topics\/project-workstations\/\"><span>Project workstations<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-foundry\/topics\/maps-and-gis\/\"><span>Maps and GIS<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-foundry\/topics\/grix-evidence\/\"><span>GRIx evidence<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-foundry\/topics\/modelling-and-simulation\/\"><span>Modelling and simulation<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-foundry\/topics\/agents-and-automation\/\"><span>Agents and automation<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-stewardship\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/ai-autonomous-systems\/\">Artificial Intelligence and Autonomous Systems<\/a> stewards artificial intelligence and machine learning for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\">Canonical page: Artificial intelligence and machine learning<\/a><\/p><\/div><\/div><\/section><section id=\"nx-s-12-sec\" class=\"nx-section\" data-hue=\"infrastructure\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Where this page leads across the Consortium.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Canonical method page<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Artificial intelligence and machine learning<\/span><span class=\"nx-dest-why\">Platform application links to the canonical method page.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/ai-autonomous-systems\/stem\/ai-ml\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Machine learning behaviour<\/span><span class=\"nx-dest-why\">Artificial Intelligence 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