Chile's Ministerio de Desarrollo Social y Familia (MDSF) developed and deployed the Sistema de Alerta Ninez (SAN), publicly known as Alerta Infancia, as part of the Acuerdo Nacional por la Infancia announced in May 2018. The system uses machine learning techniques applied to cross-referenced administrative data from multiple public institutions to estimate the risk that individual children and adolescents will suffer violations of their rights. It produces a prioritised list of at-risk children for the Oficinas Locales de la Ninez (OLN), local children's offices operated by the Subsecretaria de la Ninez, enabling earlier and more targeted intervention.
The system cross-references data that multiple public institutions collect about vulnerable minors, spanning health status, education, family context, psychosocial information, socioeconomic history, and environmental and territorial factors. Data sources include administrative databases from multiple government agencies as well as self-reported data. Additionally, field workers (sectoralistas) serving children and families can input alerts when they observe risk factors in the community, providing a human information layer alongside the algorithmic outputs.
The technical approach is described in official documentation as using advanced data analytics techniques (tecnicas de analitica avanzada) and machine learning to generate vulnerability indicators and a risk estimation for each child. OLN professionals -- coordinators, case managers (gestores), and therapists -- use the prioritised information to deliver a more comprehensive and timely response, aiming to intervene before rights violations occur rather than reacting afterwards.
The system was implemented as a pilot in 12 comunas (municipalities) by October 2019: Iquique, La Serena, San Felipe, Requinoa, Cauquenes, Concepcion, Nueva Imperial, Aysen, Santiago, La Florida, Colina, and Quillon. As of that date, 2,262 children had received the intervention, with an estimated annual capacity of approximately 3,440 interventions across the participating OLNs. The plan projected rollout to all 345 comunas of Chile within five years.
The system was a finalist in the 2019 Concurso Funciona, a government innovation competition administered by the Servicio Civil, indicating institutional recognition as a public sector innovation.
The system has attracted significant criticism. Francis Valverde, spokesperson for the Bloque por la Infancia and executive director of the Asociacion Chilena Pro Naciones Unidas (ACHNU), called it 'highly dangerous because what it is doing is marking children, stigmatizing them in practice,' questioning the criteria for determining which children are at social risk and whether adequate resources would follow the alerts. Pablo Viollier, a public policy analyst at ONG Derechos Digitales, stated that the data being collected is 'doubly sensitive' because it concerns minors who receive special protection and includes medical history, socioeconomic data, and psychosocial information which is 'very stigmatizing.' Patricia Pena, an academic at the Instituto de Comunicacion e Imagen at the Universidad de Chile, questioned how well-trained an algorithm can be to make automatic decisions about a person's life, stating that 'an algorithm does not know the details of what occurs in a family environment; the human component is necessary.'
A 2021 research report by Matias Valderrama for Derechos Digitales, supported by Canada's International Development Research Centre (IDRC), raised additional concerns including: strong technological determinism in the development process (system built first, institutional framework defined later), procurement oriented toward a single vendor with no ex-ante criteria for ethics or data justice, a socioeconomic gradient with overrepresentation of children from lower-income households, and lack of public documentation about model design and performance. The report was based on transparency law requests and secondary documentation, as MDSF explicitly refused to allow key actors to be interviewed.
No specific algorithm type, model architecture, accuracy metrics, or technology vendor has been publicly disclosed.