{"id":70387,"date":"2026-05-13T09:45:50","date_gmt":"2026-05-13T14:45:50","guid":{"rendered":"https:\/\/colombiainteligente.org\/?p=70387"},"modified":"2026-05-13T09:47:54","modified_gmt":"2026-05-13T14:47:54","slug":"ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models","status":"publish","type":"post","link":"https:\/\/colombiainteligente.org\/es_co\/tendencias\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\/","title":{"rendered":"AI for Social Risk Forecasting and Explanation: The Power of Machine Learning\u2013Based Social Risk Models"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"70387\" class=\"elementor elementor-70387\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-87c6aa2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"87c6aa2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-558bb819\" data-id=\"558bb819\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3067325c elementor-widget elementor-widget-text-editor\" data-id=\"3067325c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.14.0 - 26-06-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p style=\"margin: 0in; font-family: Calibri; font-size: 11.0pt;\">El modelamiento de riesgos sociales mediante inteligencia artificial se plantea como una alternativa para abordar la limitada capacidad de los enfoques tradicionales al analizar fen\u00f3menos complejos, din\u00e1micos y altamente interrelacionados. En contextos donde m\u00faltiples crisis coexisten, como choques clim\u00e1ticos, desplazamiento, conflictos y presiones econ\u00f3micas, la identificaci\u00f3n de patrones causales resulta particularmente desafiante. La disponibilidad creciente de datos provenientes de im\u00e1genes satelitales, plataformas digitales, indicadores econ\u00f3micos y registros geoespaciales permite capturar dimensiones previamente no cuantificadas y construir modelos que integran tanto variables objetivas como percepciones sociales.<\/p><p style=\"margin: 0in; font-family: Calibri; font-size: 11.0pt;\">\u00a0<\/p><p style=\"margin: 0in; font-family: Calibri; font-size: 11.0pt;\">El enfoque desarrollado combina m\u00e9todos de ciencia de datos con conocimiento especializado en ciencias sociales para estructurar modelos capaces de predecir cambios en fen\u00f3menos como violencia, migraci\u00f3n y criminalidad. Esta integraci\u00f3n resulta necesaria debido a que el comportamiento humano responde a interacciones no lineales entre factores econ\u00f3micos, sociales, pol\u00edticos y ambientales. La modelaci\u00f3n permite analizar millones de relaciones simult\u00e1neas, identificando aquellas variables con mayor capacidad predictiva y facilitando la comprensi\u00f3n de los factores que inciden en la evoluci\u00f3n de los riesgos. Se desarrollaron tres modelos aplicados en contextos espec\u00edficos. En la Rep\u00fablica Democr\u00e1tica del Congo, el modelo de predicci\u00f3n de conflicto alcanz\u00f3 niveles de validaci\u00f3n de 76% en Ituri, 63% en Kivu Norte y 69% en Kivu Sur, con un m\u00e1ximo de 87% en horizontes de predicci\u00f3n de 150 d\u00edas . Los resultados evidencian que variables relacionadas con percepciones sociales, como el lenguaje sobre temas sensibles, tienen una alta incidencia en la evoluci\u00f3n de la violencia, junto con factores econ\u00f3micos como inflaci\u00f3n y reservas, y din\u00e1micas hist\u00f3ricas de conflicto.<\/p><p style=\"margin: 0in; font-family: Calibri; font-size: 11.0pt;\">\u00a0<\/p><p style=\"margin: 0in; font-family: Calibri; font-size: 11.0pt;\">En el Cuerno de \u00c1frica, el modelo de migraci\u00f3n logra precisiones entre 70% y 74% en distintos horizontes temporales, apoy\u00e1ndose en la identificaci\u00f3n automatizada de estructuras construidas mediante im\u00e1genes satelitales con una precisi\u00f3n superior al 99,9%. La modelaci\u00f3n integra 32 variables agrupadas en categor\u00edas como condiciones econ\u00f3micas, clima, eventos de conflicto y percepciones sociales. Los resultados indican que la violencia explica el 39% de los cambios poblacionales, seguida por percepciones sociales con 22%, condiciones clim\u00e1ticas con 20% y variables econ\u00f3micas con 19%. Elementos geoespaciales como elevaci\u00f3n o proximidad a monta\u00f1as presentan mayor capacidad explicativa que variables clim\u00e1ticas tradicionales como la precipitaci\u00f3n. En contextos insulares con limitada disponibilidad de datos, se desarroll\u00f3 un modelo para estimar cambios en criminalidad a partir del an\u00e1lisis automatizado de noticias en medios digitales. Este enfoque genera indicadores proxy con desviaciones promedio de 12,9% respecto a los datos observados, permitiendo modelar din\u00e1micas donde no existen estad\u00edsticas oficiales frecuentes. Entre los factores m\u00e1s asociados se encuentran variaciones en precios de alimentos, as\u00ed como el volumen y tono del lenguaje en torno a temas sociales y pol\u00edticos.<\/p><p style=\"margin: 0in; font-family: Calibri; font-size: 11.0pt;\">\u00a0<\/p><p style=\"margin: 0in; font-family: Calibri; font-size: 11.0pt;\">La aplicaci\u00f3n de estos modelos tiene implicaciones directas en la formulaci\u00f3n y ejecuci\u00f3n de pol\u00edticas p\u00fablicas. La capacidad de anticipar cambios en fen\u00f3menos sociales permite orientar de manera m\u00e1s precisa la asignaci\u00f3n de recursos, dise\u00f1ar mecanismos de financiamiento basados en riesgo y fortalecer sistemas de monitoreo y evaluaci\u00f3n. Adem\u00e1s, facilita la activaci\u00f3n de instrumentos de respuesta condicionados a la evoluci\u00f3n de variables cr\u00edticas, como incrementos en violencia o desplazamiento. La integraci\u00f3n de datos multimodales y t\u00e9cnicas avanzadas de an\u00e1lisis permite superar limitaciones estructurales en la medici\u00f3n de riesgos sociales, ampliando la capacidad de las instituciones para comprender din\u00e1micas complejas y actuar de forma anticipada en contextos de alta vulnerabilidad.\u00a0<\/p><p align=\"justify\"><span style=\"color: #800000;\"><b>Para leer m\u00e1s ingrese a:<\/b><\/span><\/p><p><a href=\"https:\/\/openknowledge.worldbank.org\/entities\/publication\/d658de94-bd69-4a79-acfb-9461be639090\">https:\/\/openknowledge.worldbank.org\/entities\/publication\/d658de94-bd69-4a79-acfb-9461be639090<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t<div style=\"text-align:center\" class=\"yasr-auto-insert-visitor\"><\/div>","protected":false},"excerpt":{"rendered":"<p>El uso de modelos de Machine Learning permite anticipar cambios en fen\u00f3menos sociales complejos como violencia, migraci\u00f3n y criminalidad mediante la integraci\u00f3n de grandes vol\u00famenes de datos multimodales. A trav\u00e9s de informaci\u00f3n satelital, datos geoespaciales, indicadores econ\u00f3micos, variables clim\u00e1ticas y an\u00e1lisis de lenguaje en medios y redes sociales, se identifican patrones din\u00e1micos y factores asociados a la evoluci\u00f3n de riesgos sociales. Tres modelos aplicados en contextos de alta fragilidad logran niveles de precisi\u00f3n relevantes, con valores entre 63% y 76% para conflicto y entre 70% y 74% para cambios poblacionales, adem\u00e1s de generar indicadores proxy en entornos sin datos oficiales. La combinaci\u00f3n de variables objetivas y percepciones sociales ampl\u00eda la capacidad explicativa, incorporando elementos como precios, tensiones pol\u00edticas y narrativas p\u00fablicas. Este enfoque permite mejorar la asignaci\u00f3n de recursos, fortalecer mecanismos de respuesta temprana y optimizar la planificaci\u00f3n de pol\u00edticas p\u00fablicas en escenarios caracterizados por incertidumbre y m\u00faltiples factores interdependientes.<\/p>","protected":false},"author":23,"featured_media":70388,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"yasr_overall_rating":0,"yasr_post_is_review":"","yasr_auto_insert_disabled":"","yasr_review_type":"","footnotes":""},"categories":[55,53],"tags":[],"class_list":["post-70387","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digitalizacion","category-tendencias"],"yasr_visitor_votes":{"number_of_votes":0,"sum_votes":0,"stars_attributes":{"read_only":false,"span_bottom":false}},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI for Social Risk Forecasting and Explanation: The Power of Machine Learning\u2013Based Social Risk Models - Colombia Inteligente<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/colombiainteligente.org\/es_co\/tendencias\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI for Social Risk Forecasting and Explanation: The Power of Machine Learning\u2013Based Social Risk Models - Colombia Inteligente\" \/>\n<meta property=\"og:description\" content=\"El uso de modelos de Machine Learning permite anticipar cambios en fen\u00f3menos sociales complejos como violencia, migraci\u00f3n y criminalidad mediante la integraci\u00f3n de grandes vol\u00famenes de datos multimodales. A trav\u00e9s de informaci\u00f3n satelital, datos geoespaciales, indicadores econ\u00f3micos, variables clim\u00e1ticas y an\u00e1lisis de lenguaje en medios y redes sociales, se identifican patrones din\u00e1micos y factores asociados a la evoluci\u00f3n de riesgos sociales. Tres modelos aplicados en contextos de alta fragilidad logran niveles de precisi\u00f3n relevantes, con valores entre 63% y 76% para conflicto y entre 70% y 74% para cambios poblacionales, adem\u00e1s de generar indicadores proxy en entornos sin datos oficiales. La combinaci\u00f3n de variables objetivas y percepciones sociales ampl\u00eda la capacidad explicativa, incorporando elementos como precios, tensiones pol\u00edticas y narrativas p\u00fablicas. Este enfoque permite mejorar la asignaci\u00f3n de recursos, fortalecer mecanismos de respuesta temprana y optimizar la planificaci\u00f3n de pol\u00edticas p\u00fablicas en escenarios caracterizados por incertidumbre y m\u00faltiples factores interdependientes.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/colombiainteligente.org\/es_co\/tendencias\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\/\" \/>\n<meta property=\"og:site_name\" content=\"Colombia Inteligente\" \/>\n<meta property=\"article:published_time\" content=\"2026-05-13T14:45:50+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-13T14:47:54+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/colombiainteligente.org\/wp-content\/uploads\/2026\/05\/02-Digitalizacion-4540526.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"887\" \/>\n\t<meta property=\"og:image:height\" content=\"1253\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"DIFUSI\u00d3N COLOMBIA INTELIGENTE\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@colombiaintelig\" \/>\n<meta name=\"twitter:site\" content=\"@colombiaintelig\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"DIFUSI\u00d3N COLOMBIA INTELIGENTE\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tiempo estimado de lectura\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/colombiainteligente.org\\\/tendencias\\\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/colombiainteligente.org\\\/tendencias\\\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\\\/\"},\"author\":{\"name\":\"DIFUSI\u00d3N COLOMBIA INTELIGENTE\",\"@id\":\"https:\\\/\\\/colombiainteligente.org\\\/#\\\/schema\\\/person\\\/e207145d548b75a6d339ff1d4d3b0432\"},\"headline\":\"AI for Social Risk Forecasting and Explanation: The Power of Machine Learning\u2013Based Social Risk Models\",\"datePublished\":\"2026-05-13T14:45:50+00:00\",\"dateModified\":\"2026-05-13T14:47:54+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/colombiainteligente.org\\\/tendencias\\\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\\\/\"},\"wordCount\":631,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/colombiainteligente.org\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/colombiainteligente.org\\\/tendencias\\\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/colombiainteligente.org\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/02-Digitalizacion-4540526.webp\",\"articleSection\":[\"Digitalizaci\u00f3n\",\"Tendencias\"],\"inLanguage\":\"es-CO\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/colombiainteligente.org\\\/tendencias\\\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/colombiainteligente.org\\\/tendencias\\\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\\\/\",\"url\":\"https:\\\/\\\/colombiainteligente.org\\\/tendencias\\\/ai-for-social-risk-forecasting-and-explanation-the-power-of-machine-learning-based-social-risk-models\\\/\",\"name\":\"AI for Social Risk Forecasting and Explanation: The Power of Machine Learning\u2013Based Social Risk Models - 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A trav\u00e9s de informaci\u00f3n satelital, datos geoespaciales, indicadores econ\u00f3micos, variables clim\u00e1ticas y an\u00e1lisis de lenguaje en medios y redes sociales, se identifican patrones din\u00e1micos y factores asociados a la evoluci\u00f3n de riesgos sociales. Tres modelos aplicados en contextos de alta fragilidad logran niveles de precisi\u00f3n relevantes, con valores entre 63% y 76% para conflicto y entre 70% y 74% para cambios poblacionales, adem\u00e1s de generar indicadores proxy en entornos sin datos oficiales. La combinaci\u00f3n de variables objetivas y percepciones sociales ampl\u00eda la capacidad explicativa, incorporando elementos como precios, tensiones pol\u00edticas y narrativas p\u00fablicas. 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