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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-n15rblz1.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n15rblz1.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n15rblz1.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n15rblz1.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n15rblz1.nxe :where(ul,ol){padding-left:20px}#nxe-n15rblz1.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n15rblz1.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n15rblz1.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n15rblz1.nxe svg{display:none}#nxe-n15rblz1.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n15rblz1.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n15rblz1.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=\"dossier\" 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>A-P07 \u00b7 TOPIC \u00b7 explainability-and-fairness<\/strong>GRA 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\">GRA<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">AI and Financial Model Risk<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">In development<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 AI and Financial Model Risk<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Explainability and fairness<\/h1><p class=\"nx-tagline\">Customers, staff and supervisors need to understand why a model made a decision, and decisions must not discriminate unlawfully. Explainability methods give reasons for individual outputs and overall behaviour; fairness testing compares outcomes across groups. Both have limits, and law sets duties such as giving reasons for credit denial. This page sets out how explainability and fairness are assessed, the evidence used and who decides.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/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:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Dossier<\/strong><\/span><span class=\"nx-eyebrow\">A-P07 \u00b7 topic<\/span><\/div><ol class=\"nx-register-rows\"><li><a href=\"#nx-sec-brief-answer\"><span class=\"nx-register-n\">01<\/span><span class=\"nx-register-k\">In brief<\/span><span class=\"nx-register-v\">Definition and scope<\/span><\/a><\/li><li><a href=\"#nx-sec-question\"><span class=\"nx-register-n\">02<\/span><span class=\"nx-register-k\">The question<\/span><span class=\"nx-register-v\">4 mechanisms<\/span><\/a><\/li><li><a href=\"#nx-sec-terms\"><span class=\"nx-register-n\">03<\/span><span class=\"nx-register-k\">Key terms<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">3<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-field\"><span class=\"nx-register-n\">04<\/span><span class=\"nx-register-k\">Field<\/span><span class=\"nx-register-v\">H3 field plate<\/span><\/a><\/li><li><a href=\"#nx-sec-indicators\"><span class=\"nx-register-n\">05<\/span><span class=\"nx-register-k\">Indicators<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">6<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-pipeline\"><span class=\"nx-register-n\">06<\/span><span class=\"nx-register-k\">Pipeline<\/span><span class=\"nx-register-v\">Run graph<\/span><\/a><\/li><li><a href=\"#nx-sec-sources\"><span class=\"nx-register-n\">07<\/span><span class=\"nx-register-k\">Sources<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">5<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-decisions\"><span class=\"nx-register-n\">08<\/span><span class=\"nx-register-k\">Decision holders<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-methods\"><span class=\"nx-register-n\">09<\/span><span class=\"nx-register-k\">Methods<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">3<\/span><\/span><\/a><\/li><\/ol><p class=\"nx-register-foot\">GRA \u00b7 In development \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-sec-field\"><span>02<\/span>From a satellite tile to an exposure\u2026<\/a><a href=\"#nx-s-4-sec\"><span>03<\/span>Model risk lifecycle, in outline<\/a><a href=\"#nx-sec-question\"><span>04<\/span>Explainability and fairness: how the\u2026<\/a><a href=\"#nx-sec-terms\"><span>05<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>06<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>07<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>08<\/span>Where the evidence comes from<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"project\" 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\">Explainability and fairness in financial AI concern whether firms can give reasons for model decisions and whether outcomes treat groups lawfully and fairly. Explanation methods are approximate and fairness criteria conflict, so choices must be justified. Boards, developers, regulators and compliance functions hold the decisions.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"finance\" 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\">Spatial finance<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">From a satellite tile to an exposure update.<\/h2><p class=\"nx-lede\">How satellite radar turns a flood into an exposure update. Synthetic scene; no real event or place.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><figure class=\"nx-fk nx-sf\" data-fk=\"geoai-eo\" data-gl=\"sf:geoai-eo\" data-sf=\"geoai-eo\" data-sx-credit=\"synthetic scene and schematic model\" data-sx-src=\"Synthetic scene ~ illustrative, not data\" data-sf-learn-views=\"pipeline pr\" aria-label=\"Interactive figure: the four steps of a flood mapping workflow on a synthetic radar scene, from the tile to the detected flood and an update of affected buildings. The panel steps through or plays the sequence.\" data-nx-z=\"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\"><figcaption class=\"nx-fk-cap\"><span>How satellite radar maps a flood and updates the count of affected buildings.<\/span><span data-honesty=\"true\">Synthetic scene and schematic model; no real event or place.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/topics\/explainability-and-fairness\/#map\" data-nxf-title=\"AI and Financial Model Risk live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-s-4-sec\" class=\"nx-section\" data-hue=\"project\" 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\">Console<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Model risk lifecycle, in outline.<\/h2><p class=\"nx-lede\">The working screen behind this subject: 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-modelrisk-lifecycle\" data-cx=\"modelrisk.lifecycle\" data-variant=\"section\" aria-label=\"Model risk lifecycle: illustrative console with placeholder names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Model risk lifecycle<\/h3><p class=\"nx-fk-sub\">Explainability and fairness \u00b7 Model inventory \u00b7 Committee 3<\/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>9 validations overdue<\/span><span class=\"nx-cx-prod\">GRA \u00b7 AI and Financial Model Risk<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"steps\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Lifecycle<\/h4><\/div><\/div><ol class=\"nx-cx-steps\" data-n=\"8\"><li data-st=\"done\"><span class=\"nx-cx-sn\">Inventory<\/span><span class=\"nx-cx-snote\">registered<\/span><\/li><li data-st=\"done\"><span class=\"nx-cx-sn\">Develop<\/span><span class=\"nx-cx-snote\">documented<\/span><\/li><li data-st=\"now\"><span class=\"nx-cx-sn\">Validate<\/span><span class=\"nx-cx-snote\">independent<\/span><\/li><li data-st=\"todo\"><span class=\"nx-cx-sn\">Approve<\/span><span class=\"nx-cx-snote\">committee<\/span><\/li><li data-st=\"todo\"><span class=\"nx-cx-sn\">Implement<\/span><span class=\"nx-cx-snote\">controls<\/span><\/li><li data-st=\"todo\"><span class=\"nx-cx-sn\">Monitor<\/span><span class=\"nx-cx-snote\">thresholds<\/span><\/li><li data-st=\"todo\"><span class=\"nx-cx-sn\">Review<\/span><span class=\"nx-cx-snote\">annual<\/span><\/li><li data-st=\"todo\"><span class=\"nx-cx-sn\">Retire<\/span><span class=\"nx-cx-snote\">decommission<\/span><\/li><\/ol><\/div><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\">Models<\/h4><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-modelrisk-lifecycle-t1\"><table class=\"nx-fk-table\" data-fk-table=\"cx-modelrisk-lifecycle-t1\" aria-label=\"Models\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Model<\/button><\/th><th scope=\"col\" data-col=\"c1\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Tier<\/button><\/th><th scope=\"col\" data-col=\"c2\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Last validation<\/button><\/th><th scope=\"col\" data-col=\"c3\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Findings<\/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-modelrisk-lifecycle:r1-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>MD-169 PD retail<\/td><td><span class=\"nx-fk-pill\" data-state=\"not\"><i aria-hidden=\"true\"><\/i>Tier 1<\/span><\/td><td>19 months<\/td><td data-num=\"true\" data-sv=\"4\">4<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Overdue<\/span><\/td><\/tr><tr data-fk-key=\"cx-modelrisk-lifecycle:r1-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\" aria-current=\"true\" data-cur=\"true\"><td>MD-140 LLM complaints triage<\/td><td><span class=\"nx-fk-pill\" data-state=\"not\"><i aria-hidden=\"true\"><\/i>Tier 1<\/span><\/td><td>never<\/td><td data-num=\"true\" data-sv=\"0\">0<\/td><td><span class=\"nx-fk-pill\" data-state=\"not\"><i aria-hidden=\"true\"><\/i>Not validated<\/span><\/td><\/tr><tr data-fk-key=\"cx-modelrisk-lifecycle:r1-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>MD-400 IFRS 9 staging<\/td><td><span class=\"nx-fk-pill\" data-state=\"not\"><i aria-hidden=\"true\"><\/i>Tier 1<\/span><\/td><td>9 months<\/td><td data-num=\"true\" data-sv=\"1\">1<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Approved<\/span><\/td><\/tr><tr data-fk-key=\"cx-modelrisk-lifecycle:r1-3\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>MD-386 marketing propensity<\/td><td><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>Tier 3<\/span><\/td><td>17 months<\/td><td data-num=\"true\" data-sv=\"1\">1<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Approved<\/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\">Validation scope<\/h4><\/div><\/div><dl class=\"nx-fk-dl\"><div><dt>Conceptual soundness<\/dt><dd><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>In progress<\/span><\/dd><\/div><div><dt>Outcomes analysis<\/dt><dd><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>Pending<\/span><\/dd><\/div><div><dt>Benchmarking<\/dt><dd><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>Pending<\/span><\/dd><\/div><div><dt>Use limits<\/dt><dd>human review required<\/dd><\/div><\/dl><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Model risk lifecycle: the screen's structure, terms and states for Explainability and fairness.<\/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-question\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" 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\">The question<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Explainability and fairness: how the risk develops.<\/h2><p class=\"nx-lede\">Interpretation, bias and accountability. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"2\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Post-hoc explanations approximate<\/h3><p class=\"nx-p nx-mt-1\">Methods such as feature attributions explain a complex model approximately. Different methods can give different reasons for the same decision, and none guarantees the true cause.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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=\"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2\"\/><circle cx=\"9\" cy=\"7\" r=\"4\"\/><path d=\"M22 21v-2a4 4 0 0 0-3-3.87\"\/><path d=\"M16 3.13a4 4 0 0 1 0 7.75\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Fairness has several definitions<\/h3><p class=\"nx-p nx-mt-1\">Equal approval rates, equal error rates and calibration across groups cannot all hold at once when base rates differ. Choosing a criterion is a policy and legal decision, not a technical default.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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=\"m14.5 12.5-8 8a2.119 2.119 0 1 1-3-3l8-8\"\/><path d=\"m16 16 6-6\"\/><path d=\"m8 8 6-6\"\/><path d=\"m9 7 8 8\"\/><path d=\"m21 11-8-8\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Reasons must be specific<\/h3><p class=\"nx-p nx-mt-1\">Credit laws require specific reasons for adverse decisions. Generic reasons or ones unrelated to the actual driver do not meet the duty, whatever the model's complexity.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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=\"M2 12s3-7 10-7 10 7 10 7-3 7-10 7-10-7-10-7Z\"\/><circle cx=\"12\" cy=\"12\" r=\"3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Opacity hides harm<\/h3><p class=\"nx-p nx-mt-1\">Without explanation and outcome testing, unfair patterns surface only through complaints, litigation or enforcement, after many customers are affected.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" 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\">Key terms<\/span><\/div><h2 id=\"nx-s-6\" 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\">Feature attribution<\/td><td data-label=\"Definition\">A method that estimates how much each input contributed to a model's output.<\/td><\/tr><tr><td data-label=\"Term\">Adverse impact ratio<\/td><td data-label=\"Definition\">The ratio of favourable outcome rates between a group and a reference group.<\/td><\/tr><tr><td data-label=\"Term\">Adverse action notice<\/td><td data-label=\"Definition\">A notice telling an applicant the specific reasons credit was denied or terms worsened.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"finance\" 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\">Indicators<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">What is measured, and how well.<\/h2><p class=\"nx-lede\">Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Indicator<\/th><th scope=\"col\">Unit<\/th><th scope=\"col\">Cadence<\/th><th scope=\"col\">Source type<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Indicator\">Adverse impact ratio<\/td><td data-label=\"Unit\">ratio of favourable outcome rates between groups<\/td><td data-label=\"Cadence\">periodic<\/td><td data-label=\"Source type\">Fairness testing<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Error rate differences across groups<\/td><td data-label=\"Unit\">difference in false positive or negative rates<\/td><td data-label=\"Cadence\">periodic<\/td><td data-label=\"Source type\">Fairness testing<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Explanation stability<\/td><td data-label=\"Unit\">agreement between explanation methods<\/td><td data-label=\"Cadence\">per validation<\/td><td data-label=\"Source type\">Validation testing<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Adverse action notices citing model reasons<\/td><td data-label=\"Unit\">share of declines with specific reasons<\/td><td data-label=\"Cadence\">periodic<\/td><td data-label=\"Source type\">Compliance review<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Complaints alleging unfair automated decisions<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">quarterly<\/td><td data-label=\"Source type\">Complaints data<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Enforcement actions on algorithmic discrimination<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Regulator records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OFFICIAL<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/topics\/explainability-and-fairness\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" 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\">Canvas<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">From sources to decisions.<\/h2><p class=\"nx-lede\">The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Pipeline canvas<\/strong> \u00b7 Explainability and fairness as a run graph<\/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-a-p07-topics-explainability-and-fairness--t nx-dag-a-p07-topics-explainability-and-fairness--d\" data-nx-z=\"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\"><title id=\"nx-dag-a-p07-topics-explainability-and-fairness--t\">Explainability and fairness: AI risk management, govern to manage<\/title><desc id=\"nx-dag-a-p07-topics-explainability-and-fairness--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. 02<\/strong><span>Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-sources\" class=\"nx-section\" data-hue=\"finance\" 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\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Sources<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Where the evidence comes from.<\/h2><p class=\"nx-lede\">Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Source<\/th><th scope=\"col\">What it gives<\/th><th scope=\"col\">Licence class<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Source\">CFPB guidance on adverse action notices and AI<\/td><td data-label=\"What it gives\">United States duty to give specific reasons<\/td><td data-label=\"Licence class\">Public domain<\/td><\/tr><tr><td data-label=\"Source\">MAS FEAT principles and Veritas methodology documents<\/td><td data-label=\"What it gives\">Fairness assessment methods for financial services<\/td><td data-label=\"Licence class\">Free, MAS terms<\/td><\/tr><tr><td data-label=\"Source\">IAIS Application Paper on the supervision of AI<\/td><td data-label=\"What it gives\">Fairness, transparency and redress expectations for insurers<\/td><td data-label=\"Licence class\">Free, IAIS terms<\/td><\/tr><tr><td data-label=\"Source\">EIOPA report on governance principles for ethical and trustworthy AI<\/td><td data-label=\"What it gives\">Insurance sector AI fairness and governance<\/td><td data-label=\"Licence class\">Free, EU reuse terms<\/td><\/tr><tr><td data-label=\"Source\">Academic literature on fairness definitions and impossibility results<\/td><td data-label=\"What it gives\">Formal analysis of fairness criteria<\/td><td data-label=\"Licence class\">Free to read, terms per publisher<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/topics\/explainability-and-fairness\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" 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\">Decisions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">AI and Financial Model Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"ledger\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Who decides<\/th><th scope=\"col\">The question<\/th><th scope=\"col\">Evidence this page serves<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Who decides\">Boards and conduct committees<\/td><td data-label=\"The question\">Which fairness criteria apply to each use<\/td><td data-label=\"Evidence this page serves\">Legal requirements and outcome testing<\/td><\/tr><tr><td data-label=\"Who decides\">Model developers and validators<\/td><td data-label=\"The question\">Which explanation methods are reliable for a model<\/td><td data-label=\"Evidence this page serves\">Stability and fidelity testing<\/td><\/tr><tr><td data-label=\"Who decides\">Conduct and equality regulators<\/td><td data-label=\"The question\">Whether outcomes are lawful<\/td><td data-label=\"Evidence this page serves\">Outcome data, complaints and testing evidence<\/td><\/tr><tr><td data-label=\"Who decides\">Compliance functions<\/td><td data-label=\"The question\">Whether customer explanations meet legal duties<\/td><td data-label=\"Evidence this page serves\">Notice reviews and model reason codes<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Guide to fairness criteria and their trade-offs<\/li><li>Method note on testing explanation methods for stability<\/li><li>Brief on adverse action reasons for complex models<\/li><li>Questions for boards on fairness criterion choice<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"finance\" 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\">Core questions<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">What experts ask.<\/h2><p class=\"nx-lede\">The questions concern understanding, justice and responsibility.<\/p><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>Can developers, validators, users and customers understand why a model made a decision?<\/li><li>Which fairness definition is appropriate for the decision, and who chose it?<\/li><li>Where does bias enter: data, design, deployment or feedback?<\/li><li>Can a customer challenge an automated decision and obtain redress?<\/li><li>Who is accountable when an unexplainable model causes harm?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms\" class=\"nx-section\" data-hue=\"project\" 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\">Mechanisms<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Mechanisms and drivers.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">Simple models such as scorecards are directly interpretable. Complex models, such as gradient-boosted trees and neural networks, require post-hoc explanation methods that approximate their behaviour; these approximations can be unstable or misleading. Explainability needs differ by audience: a validator needs technical depth, a customer needs a clear reason.<\/p><p class=\"nx-p\">Bias arises from historical data, unrepresentative samples, proxy variables and feedback loops in which model decisions shape future data. Fairness has several mathematical definitions, such as equal outcomes, equal error rates or equal calibration, which generally cannot all be satisfied at once. Choosing among them is a value judgement that should be made explicitly and accountably.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods and data<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">How it is studied, and where the methods stop.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Method or data<\/th><th scope=\"col\">What it shows<\/th><th scope=\"col\">Limits<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Method or data\">Interpretable model design<\/td><td data-label=\"What it shows\">Inherent transparency<\/td><td data-label=\"Limits\">May sacrifice some predictive performance<\/td><\/tr><tr><td data-label=\"Method or data\">Post-hoc explanation methods<\/td><td data-label=\"What it shows\">Feature contributions to outputs<\/td><td data-label=\"Limits\">Approximate and sometimes unstable<\/td><\/tr><tr><td data-label=\"Method or data\">Fairness testing across groups<\/td><td data-label=\"What it shows\">Disparities in outcomes and errors<\/td><td data-label=\"Limits\">Requires group data that may be unavailable<\/td><\/tr><tr><td data-label=\"Method or data\">Impact assessment<\/td><td data-label=\"What it shows\">Consequences for affected people<\/td><td data-label=\"Limits\">Relies on foresight and engagement<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-14\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">13<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods<\/span><\/div><h2 id=\"nx-s-14\" class=\"nx-section-title\">How it is studied, and what the methods cannot say.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-methods\" data-limits=\"true\"><ol class=\"nx-mledger\" aria-label=\"Methods\"><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/ai-assurance\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">AI evaluation and assurance engineering<\/span><span class=\"nx-mledger-role\">Validation of machine learning models \u00b7 Red-teaming generative AI \u00b7 Fairness assessment<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/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><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/engineering\/ai-assurance\/\" aria-label=\"AI evaluation and assurance engineering: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/ai-ml\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Artificial intelligence and machine learning<\/span><span class=\"nx-mledger-role\">Gradient-boosted credit models \u00b7 Language models in operations \u00b7 Anomaly detection<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/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><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\" aria-label=\"Artificial intelligence and machine learning: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Calibration testing \u00b7 Drift statistics \u00b7 Group outcome comparison<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/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><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\" aria-label=\"Probability and statistics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><\/ol><aside class=\"nx-limits\" aria-label=\"What the methods cannot say\"><span class=\"nx-eyebrow\">What the methods cannot say<\/span><p class=\"nx-limits-text\">Fairness testing needs data on protected characteristics that firms often do not hold, so groups are inferred with error. Explanation methods are approximations and can be unstable. Legal standards differ by jurisdiction and product. Choosing among fairness criteria involves value judgements that evidence cannot settle. AI and Financial Model Risk explains methods; it gives no legal advice and assesses no firm.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-15-sec\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-15\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">14<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-15\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>Can a model satisfy every fairness definition?<\/summary><div class=\"nx-answer\">Generally not when groups have different underlying outcome rates; research shows common criteria conflict, so a choice has to be made and justified.<\/div><\/details><details><summary>Are feature attributions true explanations?<\/summary><div class=\"nx-answer\">They approximate how inputs influenced an output. They help but can disagree between methods, so their stability and fidelity should be tested.<\/div><\/details><details><summary>Does using AI change the duty to explain credit decisions?<\/summary><div class=\"nx-answer\">No. United States guidance states that creditors must give specific and accurate reasons for adverse action whatever the technology used.<\/div><\/details><details><summary>Is a more accurate model always better?<\/summary><div class=\"nx-answer\">Not necessarily. A slightly less accurate model that is interpretable and fairer may be more appropriate, depending on the decision and the law.<\/div><\/details><details><summary>Can fairness be fully automated?<\/summary><div class=\"nx-answer\">No. Choosing a fairness definition involves value judgements and legal considerations that require human accountability.<\/div><\/details><details><summary>Who decides which fairness definition applies?<\/summary><div class=\"nx-answer\">The institution, within the law of the jurisdictions where it operates and under the oversight of its regulators. The choice should be documented, justified and reviewed, because it determines whose errors the model tolerates.<\/div><\/details><details><summary>Does the platform certify models as fair?<\/summary><div class=\"nx-answer\">No. GRA does not certify, validate or rate models.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-16\" class=\"nx-h3 nx-mt-2\">Around AI and Financial Model Risk.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/model-risk-governance\/\">Model risk governance <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 href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/credit-and-pricing-models\/\">Credit and pricing models <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 href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/generative-ai-in-finance\/\">Generative AI in finance <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 href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/model-monitoring-and-drift\/\">Model monitoring and drift <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 href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/human-in-the-loop-controls\/\">Human in the loop controls <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 href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/third-party-models\/\">Third party models <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 href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/\">AI and Financial Model Risk home <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 href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/counterparts\/\">Counterparts across the Consortium <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 href=\"https:\/\/globalriskalliance.com\/\">The Global Risks Alliance home <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 href=\"https:\/\/globalriskalliance.com\/trust\/\">GRA governance and trust <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><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-17\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">AI and Financial Model Risk<\/span><h2 id=\"nx-close-17\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. 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=\"mailto:contact@globalriskalliance.com?subject=AI%20and%20Financial%20Model%20Risk%3A%20Explainability%20and%20fairness\">Write to the team <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:\/\/globalriskalliance.com\/ai-financial-model-risk\/contact\/\">See all contact routes<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/explainability-and-fairness\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/explainability-and-fairness\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Can a model satisfy every fairness definition?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Generally not when groups have different underlying outcome rates; research shows common criteria conflict, so a choice has to be made and justified.\"}},{\"@type\":\"Question\",\"name\":\"Are feature attributions true explanations?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"They approximate how inputs influenced an output. 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