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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n9rmfdnl.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-scene nx-es-scene\" data-scene=\"text\" data-ground=\"none\" data-es-org=\"gcri\" aria-labelledby=\"nx-scene-title\"><div class=\"nx-scene-intro nx-es-intro\"><p class=\"nx-eyebrow\"><abbr title=\"The Global Centre for Risk and Innovation\">GCRI<\/abbr> \u00b7 Labs<\/p><span class=\"nx-scene-rule\" aria-hidden=\"true\"><\/span><h1 id=\"nx-scene-title\" class=\"nx-scene-title\">Machine learning in laboratory science<\/h1><p class=\"nx-scene-lede\">Machine learning is both a tool and an object of study in this lab. It assists literature screening, experimental design, image and sequence analysis and simulation, and it needs its own evaluation discipline: leakage, <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-h95\" data-tc=\"NHS-1 tech.ai-failure.distribution-shift-failure\" data-tk=\"Frontier and exponential technology \u00b7 AI system failure and misalignment\" data-td=\"A model meeting conditions unlike its training data, such as a novel disaster or market regime, and giving confident but wrong outputs exactly when reliable judgement matters most.\" data-ts=\"NRS-H95\" data-tr=\"draft\">distribution shift<\/a> and opaque failure modes are research risks to be tested, not assumed away.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/nexus-labs\/write-to-us\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/nexus-labs\/stem\/\">Back to STEM foundation of Labs<\/a><\/div><\/div><\/section><section id=\"nx-s-1-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-1\"><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\">Contents<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Machine learning in laboratory science in five parts.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"protocol\"><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\">5 steps<\/span><\/div><ol><li><a href=\"#nx-sec-application\"><span class=\"nx-lv-steps-n\">01<\/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-data\"><span class=\"nx-lv-steps-n\">02<\/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\">03<\/span><span class=\"nx-lv-steps-t\">Validation<\/span><span class=\"nx-lv-steps-d\">How results are checked<\/span><\/a><\/li><li><a href=\"#nx-sec-connections\"><span class=\"nx-lv-steps-n\">04<\/span><span class=\"nx-lv-steps-t\">Connections<\/span><span class=\"nx-lv-steps-d\">Topics that rely on this method<\/span><\/a><\/li><li><a href=\"#nx-sec-stewardship\"><span class=\"nx-lv-steps-n\">05<\/span><span class=\"nx-lv-steps-t\">Stewardship<\/span><span class=\"nx-lv-steps-d\">Who maintains the method<\/span><\/a><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-2\"><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\">Application<\/span><\/div><h2 id=\"nx-s-2\" 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 research and evidence, methods and modelling and innovation, machine learning can scale detection and forecasting across interconnected <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/systems\" data-tc=\"Systems\" data-tk=\"Nexus Standards\" data-td=\"The twenty Nexus systems, NXS-CMD to NXS-LAB\">systems<\/a>. 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=\"M14 2v4a2 2 0 0 0 2 2h4\"\/><path d=\"M4.268 21a2 2 0 0 0 1.727 1H18a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v3\"\/><path d=\"m9 18-1.5-1.5\"\/><circle cx=\"5\" cy=\"14\" r=\"3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Assisted screening for systematic reviews<\/h3><p class=\"nx-p nx-mt-1\">Classifiers prioritise records for human screening; recall is measured against a fully screened sample, and the stopping rule is stated in the review protocol before screening starts.<\/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\">Surrogate models to guide experiments<\/h3><p class=\"nx-p nx-mt-1\">Models trained on simulations or past runs suggest which experiments to run next, using uncertainty to balance exploration and confirmation, and every suggestion is tested physically before it counts.<\/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=\"M6 18h8\"\/><path d=\"M3 22h18\"\/><path d=\"M14 22a7 7 0 1 0 0-14h-1\"\/><path d=\"M9 14h2\"\/><path d=\"M9 12a2 2 0 0 1-2-2V6h6v4a2 2 0 0 1-2 2Z\"\/><path d=\"M12 6V3a1 1 0 0 0-1-1H9a1 1 0 0 0-1 1v3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Image and signal analysis in the lab<\/h3><p class=\"nx-p nx-mt-1\">Segmentation and classification models are validated against expert annotation from other sites and instruments, with failure cases catalogued and shared alongside the model.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-s-3-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-3\"><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\">Monitor<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Model training.<\/h2><p class=\"nx-lede\">The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-labs-method-ml\" data-cx=\"labs.method-ml\" data-variant=\"section\" aria-label=\"Model training: illustrative console with example 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\">Labs \u00b7 Machine learning in laboratory \u00b7 Run R-4300<\/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&#8217;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 Labs<\/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-labs-method-ml-t0\"><table class=\"nx-fk-table\" data-fk-table=\"cx-labs-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-labs-method-ml:r0-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>All<\/td><td data-num=\"true\" data-sv=\"0.79\">0.79<\/td><td data-num=\"true\" data-sv=\"0.04\">0.04<\/td><td data-num=\"true\" data-sv=\"7294\">7294<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Pass<\/span><\/td><\/tr><tr data-fk-key=\"cx-labs-method-ml:r0-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Region A<\/td><td data-num=\"true\" data-sv=\"0.84\">0.84<\/td><td data-num=\"true\" data-sv=\"0.04\">0.04<\/td><td data-num=\"true\" data-sv=\"1004\">1004<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Review<\/span><\/td><\/tr><tr data-fk-key=\"cx-labs-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=\"0.79\">0.79<\/td><td data-num=\"true\" data-sv=\"0.12\">0.12<\/td><td data-num=\"true\" data-sv=\"594\">594<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Review<\/span><\/td><\/tr><tr data-fk-key=\"cx-labs-method-ml:r0-3\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Rare class<\/td><td data-num=\"true\" data-sv=\"0.67\">0.67<\/td><td data-num=\"true\" data-sv=\"0.14\">0.14<\/td><td data-num=\"true\" data-sv=\"67\">67<\/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 machine learning<\/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-labs-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||0:47.6;1:50;2:46.6;3:48.5;4:50.1;5:53.8;6:58;7:62.5;8:68.6;9:71.2;10:75.5;11:82.1;12:82.2;13:79.5;14:77.1;15:81.8;16:84.3;17:88.5;18:91.2;19:94;20:91.7;21:94;22:94;23:94\" 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&#8217;s structure, terms and states for Labs.<\/span><span data-honesty=\"true\">Example names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-4\"><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\">Data<\/span><\/div><h2 id=\"nx-s-4\" 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-labs\/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\">Annotated benchmark datasets<\/td><td data-label=\"Role\">Training and evaluation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Laboratory instrument outputs<\/td><td data-label=\"Role\">Inputs to analysis models<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Simulation archives<\/td><td data-label=\"Role\">Training surrogate models<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Model and dataset cards<\/td><td data-label=\"Role\">Intended use and known limits<\/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\/G08\/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\/G08\/stem\/ai-ml\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-audience\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-5\"><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\">Methods and evidence<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">How machine learning in laboratory science is modelled, measured and tested.<\/h2><p class=\"nx-lede\">The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-au\" data-au-voice=\"science\" data-au-n=\"1\"><figure class=\"nx-au-c\" data-au-fig=\"method-panel\"><h3 class=\"nx-h4\">Model, equations and assumptions<\/h3><p class=\"nx-p nx-au-what\">The model structure used for machine learning in laboratory science, the assumptions it makes and how each is tested.<\/p><div class=\"nx-au-eqs\"><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Risk of element i<\/span><code>R<sub>i<\/sub> = f(H<sub>i<\/sub>, E<sub>i<\/sub>, V<sub>i<\/sub>, C<sub>i<\/sub>)<\/code><span class=\"nx-au-n\">hazard, exposure, vulnerability and coping capacity<\/span><\/div><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Expected annual loss<\/span><code>EAL = &#8721;<sub>k<\/sub> p<sub>k<\/sub> &#183; L<sub>k<\/sub><\/code><span class=\"nx-au-n\">over events k with annual probability p<sub>k<\/sub><\/span><\/div><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Coupled systems<\/span><code>x<sub>t+1<\/sub> = A x<sub>t<\/sub> + B u<sub>t<\/sub> + &#949;<sub>t<\/sub><\/code><span class=\"nx-au-n\">A carries the couplings, u the shock, &#949; the error<\/span><\/div><\/div><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Assumption<\/th><th scope=\"col\">Why it is made<\/th><th scope=\"col\">How it is tested<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Assumption\">Hazard and exposure are independent within an event<\/td><td data-label=\"Why it is made\">Keeps the loss integral tractable<\/td><td data-label=\"How it is tested\">Compare with a copula fit on joint records<\/td><\/tr><tr><td data-label=\"Assumption\">Couplings are linear near the operating point<\/td><td data-label=\"Why it is made\">Lets shocks propagate through A<\/td><td data-label=\"How it is tested\">Perturb beyond the linear range and compare<\/td><\/tr><tr><td data-label=\"Assumption\">Vulnerability curves transfer between places<\/td><td data-label=\"Why it is made\">Local curves are rarely available<\/td><td data-label=\"How it is tested\">Validate against observed losses where they exist<\/td><\/tr><\/tbody><\/table><\/div><dl class=\"nx-au-kv\"><div><dt>Inputs<\/dt><dd>versioned datasets with provenance<\/dd><\/div><div><dt>Outputs<\/dt><dd>distributions, never single numbers<\/dd><\/div><div><dt>Code<\/dt><dd>released with the model card and tests<\/dd><\/div><div><dt>Reproduction<\/dt><dd>same data, code, environment and seed<\/dd><\/div><\/dl><figcaption class=\"nx-caption\"><span>Standard risk and state-space formulations; the assumptions listed are typical of the method, stated for review, not results.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-chips nx-au-fw\" aria-label=\"Frameworks cited\"><span class=\"nx-chip\">FAIR guiding principles \u00b7 GO FAIR and FORCE11<\/span><span class=\"nx-chip\">W3C PROV \u00b7 World Wide Web Consortium<\/span><span class=\"nx-chip\">Guidance on consistent treatment of uncertainties \u00b7 IPCC<\/span><span class=\"nx-chip\">CEOS Analysis Ready Data \u00b7 Committee on Earth Observation Satellites<\/span><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-6\"><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\">Validation<\/span><\/div><h2 id=\"nx-s-6\" 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\">Leakage audit<\/td><td data-label=\"How it is done\">Splits are made by subject, site or time as the question requires, and preprocessing is fitted on training data only<\/td><\/tr><tr><td data-label=\"Check\">External validation<\/td><td data-label=\"How it is done\">Performance is reported on data from a different source or period<\/td><\/tr><tr><td data-label=\"Check\">Calibration and robustness<\/td><td data-label=\"How it is done\">Probabilities are checked for calibration, and performance under plausible perturbations is reported<\/td><\/tr><tr><td data-label=\"Check\">Reporting checklist<\/td><td data-label=\"How it is done\">Studies are reported against REFORMS, TRIPOD+AI or the relevant domain checklist<\/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-s-7-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-7\"><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\">Process<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">How the process runs.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-pe\" id=\"nx-pe-ai\" data-nx-pe=\"nx-pe-ai\" data-active=\"ai-rmf\"><div class=\"nx-pe-shell\"><p class=\"nx-pe-eb\">Process<\/p><h2 class=\"nx-pe-title\">Machine learning in laboratory science: AI risk management (NIST)<\/h2><p class=\"nx-pe-cap\">The AI RMF Core has four functions, GOVERN, MAP, MEASURE and MANAGE, with GOVERN cross-cutting; the order drawn is one common reading, not a checklist.<\/p><ol class=\"nx-pe-steps\"><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">01 Govern<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">01<\/span><span class=\"nx-pe-st\">GOVERN: policies, roles and accountability<\/span><span class=\"nx-pe-k\">Step \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">GOVERN is a cross-cutting function. It is infused throughout AI risk management and enables the other functions.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">02 Map<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">02<\/span><span class=\"nx-pe-st\">MAP: context and intended use<\/span><span class=\"nx-pe-k\">Dataset<\/span><span class=\"nx-pe-sm\">The MAP function establishes the context to frame risks related to an AI system, including intended purposes and impacts on people.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">03 Measure<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">03<\/span><span class=\"nx-pe-st\">MEASURE: evaluate trustworthy characteristics<\/span><span class=\"nx-pe-k\">Model<\/span><span class=\"nx-pe-sm\">AI <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/systems\" data-tc=\"Systems\" data-tk=\"Nexus Standards\" data-td=\"The twenty Nexus systems, NXS-CMD to NXS-LAB\">systems<\/a> are evaluated for trustworthy characteristics. They should be tested before deployment and regularly in operation.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">04<\/span><span class=\"nx-pe-st\">MEASURE: track identified risks over time<\/span><span class=\"nx-pe-k\">Model<\/span><span class=\"nx-pe-sm\">Mechanisms for tracking identified AI risks over time are in place, including unanticipated and emergent risks.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">04 Manage<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">05<\/span><span class=\"nx-pe-st\">MANAGE: treat risks<\/span><span class=\"nx-pe-k\">Step<\/span><span class=\"nx-pe-sm\"><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 risk-treatment\" data-tk=\"Nexus risk vocabulary\" data-td=\"Selecting and applying options to modify risk: avoiding, reducing, transferring, sharing or retaining it.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Risk treatment<\/a> plans cover response to, recovery from and communication about incidents. Resources go to mapped and measured risks.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">06<\/span><span class=\"nx-pe-st\">Proceed or not (MANAGE 1.1)<\/span><span class=\"nx-pe-k\">Gate \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">A determination is made whether the system meets its purposes and objectives, and whether to proceed. The framework sets no thresholds.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">07<\/span><span class=\"nx-pe-st\">Deployment decision<\/span><span class=\"nx-pe-k\">Decision \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">The organisation that owns the system decides whether and under what conditions to deploy.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">08<\/span><span class=\"nx-pe-st\">Post-deployment monitoring and incidents<\/span><span class=\"nx-pe-k\">Model<\/span><span class=\"nx-pe-sm\">Post-deployment monitoring plans are implemented. Incidents and errors are shared with relevant AI actors, including affected communities.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">05 Record<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">09<\/span><span class=\"nx-pe-st\">Inventory of AI systems<\/span><span class=\"nx-pe-k\">Dataset<\/span><span class=\"nx-pe-sm\">GOVERN 1.6 asks for mechanisms to inventory AI systems. A model card is common practice and is not named in the framework.<\/span><\/li><\/ol><\/li><\/ol><p class=\"nx-pe-foot\"><span>Neutral outline of a published external standard or method, from its issuing body. Not a record of operation. Nexus does not perform, certify or approve any step shown. A gate or decision belongs to the party the source names. The actions are not necessarily an ordered set of steps. The EU AI Act is not drawn in this flow.<\/span><\/p><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-8\"><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\">Connections<\/span><\/div><h2 id=\"nx-s-8\" 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-labs\/topics\/ai-and-scientific-workflows\/\"><span>AI in scientific workflows<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-work-lab\/\"><span>Future of work lab<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-media-lab\/\"><span>Future of media lab<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-energy-and-water-labs\/\"><span>Future of energy and water labs<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-health-and-bio-labs\/\"><span>Future of health and biotechnology labs<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/reproducibility-and-negative-results\/\"><span>Reproducibility and negative results<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><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-9\"><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\">Stewardship<\/span><\/div><h2 id=\"nx-s-9\" 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-10-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-10\"><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\">Connected<\/span><\/div><h2 id=\"nx-s-10\" 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\/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 and Autonomous Systems stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-11-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-11\"><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\">Questions<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of artificial intelligence and machine learning in this field?<\/summary><div class=\"nx-answer\">Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.<\/div><\/details><details><summary>Are the values on this page real?<\/summary><div class=\"nx-answer\">The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method&#8217;s use, not a run; no result is reported here.<\/div><\/details><details><summary>Where else is this method applied?<\/summary><div class=\"nx-answer\">The <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\">canonical method page<\/a> lists every platform that applies it.<\/div><\/details><\/div><\/div><\/div><\/section><section id=\"nx-sec-nr-gateway\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-nr-gateway-h\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">Reports<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contribute<\/span><\/div><h2 id=\"nx-nr-gateway-h\" class=\"nx-section-title\">Contribute evidence from this page.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/reports\">Reports<\/a> is the Consortium&#8217;s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.<\/p><div class=\"nx-chips\"><span class=\"nx-chip\">GCRI<\/span><span class=\"nx-chip\">Labs<\/span><span class=\"nx-chip\">Labs Evidence<\/span><span class=\"nx-chip\">Methods and Technical<\/span><\/div><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/nexus-labs\/write-to-us\/contact\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Email<\/span><span class=\"nx-dest-host\">Gateway GW-GCRI<\/span><\/span><span class=\"nx-dest-title\">Send it from this page<\/span><span class=\"nx-dest-why\">Evidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/nexus-reports\/open-science\/#nx-sec-zenodo\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Zenodo<\/span><span class=\"nx-dest-host\">Open deposit<\/span><\/span><span class=\"nx-dest-title\">Deposit it and submit to the community<\/span><span class=\"nx-dest-why\">Publish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/nexus-reports\/contribute\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Reports<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">See every way to contribute<\/span><span class=\"nx-dest-why\">The contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><div class=\"nx-live-slot\" data-nxf-slot=\"contribute\" data-nxf-kind=\"workflow\" data-nxf-resource=\"nexus:\/\/gcri\/nexus-labs\/stem\/ai-ml#contribute\" data-nxf-title=\"Contribute evidence\" data-nxf-height=\"480\" hidden><\/div><p class=\"nx-note nx-mt-3\">Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.<\/p><\/div><\/div><\/section><section class=\"nx-section nx-deeper-band\" aria-labelledby=\"nx-deeper-therisk-global-nexus-labs-stem-ai-ml-h\"><div class=\"nx-container\"><nav class=\"nx-deeper\" aria-labelledby=\"nx-deeper-therisk-global-nexus-labs-stem-ai-ml-h\" data-nx-deeper=\"3\" data-nx-resources=\"1 1 1\"><h2 class=\"nx-deeper-h\" id=\"nx-deeper-therisk-global-nexus-labs-stem-ai-ml-h\">Go deeper<\/h2><ul><li><a href=\"https:\/\/docs.therisk.global\/organization\/systems\/nxs-dss\" data-ext><span class=\"nx-deeper-k\">Documentation:<\/span> Nxs-dss<span class=\"nx-deeper-x\" aria-hidden=\"true\">\u2197<\/span><span class=\"nx-deeper-sr\"> (opens another site)<\/span><\/a><\/li><li><a href=\"https:\/\/risksacademy.com\/technology-digital\/artificial-intelligence\/\" data-ext><span class=\"nx-deeper-k\">Academy:<\/span> Artificial intelligence and machine learning<span class=\"nx-deeper-x\" aria-hidden=\"true\">\u2197<\/span><span class=\"nx-deeper-sr\"> (opens another site)<\/span><\/a><\/li><li><a href=\"https:\/\/nexustandards.com\/open-infrastructure\/registries-and-resolver\/\" data-ext><span class=\"nx-deeper-k\">Standards:<\/span> Registries and the resolver<span class=\"nx-deeper-x\" aria-hidden=\"true\">\u2197<\/span><span class=\"nx-deeper-sr\"> (opens another site)<\/span><\/a><\/li><\/ul><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-12\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Labs<\/span><h2 id=\"nx-close-12\" class=\"nx-section-title\">Bring a method, a dataset or a question.<\/h2><p class=\"nx-lede\">Information and enquiries are open today. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/write-to-us\/research-collaboration\/\">Propose a method <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/nexus-labs\/contact\/\">See all contact routes<\/a><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-legal-13\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Legal<\/span><h2 id=\"nx-legal-13\" class=\"nx-h3 nx-mt-2\">Legal documents.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Legal documents\"><a href=\"https:\/\/therisk.global\/legal\/terms\/\">Platform Terms of Use <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/privacy\/\">Platform Privacy Notice <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/cookies\/\">Platform Cookie and Storage Notice <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/legal-notice\/\">Legal Notice <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/acceptable-use\/\">Acceptable Use Policy <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/participation-terms\/\">Participation and Events Terms <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/contribution-terms\/\">Contribution Terms <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/research-and-data-terms\/\">Research and Data Terms <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/ai-use\/\">AI-use Statement <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/intellectual-property\/\">Intellectual Property and Licensing <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/trademark-policy\/\">Trademark and Name Use Policy <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/disclaimer\/\">Disclaimers and Data Notices <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/corrections\/\">Corrections and Right of Reply Policy <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/complaints-and-integrity\/\">Complaints and integrity reporting <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/security\/\">Security and responsible disclosure <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/code-of-conduct\/\">Code of Conduct <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/trust\/accessibility\/\">Accessibility Statement <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/legal\/third-party-notices\/\">Third-party notices and attributions <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><p class=\"nx-note nx-mt-3\">Maps, data and figures on this site are shown for context and general information. 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