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Impacto de la tecnología de reconocimiento facial en la prevención del crimen.

 Impact of facial recognit= ion technology on crime prevention

 

 


= 1

Ligia Piedad Carvajal Ibarra=

 

https://orcid.org/0000-0003-4940-3137

 

 

Licenciada en Laboratorio Clínico, Estudiante de la Maestría en Criminalística y Cienci= as Forenses, Universidad Nacional de Chimborazo, Riobamba, Ecuador.

ligia77@live.com

= 2

Diego Lenin Andrade Ulloa=

 

/ https://orcid.org/= 0009-0000-9349-5732

 

 

Doctor en Jurisprudencia Magíster  en De= recho Procesal Penal, Magíster en Derecho

Digital Especialista en Derecho Constitucional y Administrativo  Posgrado Universidad Nacional de Chim= borazo, Riobamba, Ecuador

dandrade@u= nach.edu.ec

 <= /span>

 

 

&nbs= p;

&nbs= p;

 

 

 

 

Artículo de Investigación Científica y Tecnológica

Enviado: 09/06/20= 24

Revisado: 15/06/20= 24

Aceptado: 01/07/202= 4

Publicado: 05/07/202= 4

DOI:  =     https://doi.= org/10.33262/anatomiadigital.v7i2.2.3113    

=  

 

 

Cítese:

 

 

Carvajal Ibarra, L. P., & Andrade Ul= loa, D. L. (2024). Impacto de la tecnología de reconocimiento facial en la prevención del crimen. Anatomía Digital, 7(2.2), 209-221. https://doi.= org/10.33262/anatomiadigital.v7i2.2.3113

 

 

 

ANATOMÍA DIGITAL= , es una Revista Electrónica, Trimestral, que se publicará en soporte electrón= ico tiene como misión contribuir a la   formación de profesionales competentes con visión humanística y crítica que sean capaces de exp= oner sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos en la sociedad. = https://anatomiadigital.org  

La revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registra= da en la Cámara Ecuatoriana de Libro con No de Afiliación 663) www.celibro.org.ec

 

 

 

Esta revista está protegida bajo una licencia Creative Commons Atribución-No Comercial - Compartir Igual 4.0 Internatio= nal. Copia de la licencia: https://creativecommons.org/licens= es/by-nc-sa/4.0/deed.es

 

Palabras claves: <= /p>

Reconocimiento facial

Prevención del delito

Ética=

Derechos humanos=

Vigilancia<= /o:p>

 

Resumen

Intr= oducción: El reconocimiento facial es una tecnolo= gía de inteligencia artificial que permite identificar individuos de manera automatizada a partir de imágenes o videos de sus rostros. Su uso por par= te de agencias policiales y de seguridad para prevenir el crimen ha aumentado rápidamente en años recientes. Sin embargo, esta aplicación genera importantes preocupaciones éticas relacionadas con la privacidad, la protección de datos personales, la libertad de pensamiento, la presunción= de inocencia y la no discriminación. Objetivo: Evaluar la eficacia y = las implicaciones éticas de la implementación de tecnologías de reconocimiento facial en entornos urbanos para la prevención del crimen, proporcionando recomendaciones fundamentadas para una aplicación responsable. Metodol= ogía: Se realizó una revisión sistemática cualitativa de literatura científica publicada entre 2017 y 2022 sobre el uso de reconocimiento facial en la prevención del delito. Se analizaron 16 artículos que cumplieron criterio= s de elegibilidad predefinidos, provenientes de revistas académicas de inteligencia artificial, derecho, criminología y ética. Resultados: Los estudios revisados sugieren que el reconocimiento facial está siendo crecientemente adoptado por fuerzas de seguridad en espacios públicos. Si bien los algoritmos han alcanzado altos niveles de precisión, la evidencia sobre su efectividad para reducir el crimen es aún limitada y mixta. Al m= ismo tiempo, surgen importantes riesgos éticos relacionados con potenciales vulneraciones a derechos fundamentales, sesgos algorítmicos, vigilancia masiva y erosión de libertades civiles. Expertos plantean la necesidad de regulaciones estrictas, evaluaciones de impacto, supervisión independient= e y debate público para prevenir abusos. Conclusión: El reconocimiento facial representa una herramienta prometedora pero controversial para prevenir el delito. Su eficacia real es aún incierta y sus riesgos éticos= son significativos. Se requiere un enfoque precautorio, con salvaguardas lega= les y técnicas robustas, monitoreo constante y deliberación social amplia para equilibrar seguridad y libertad. Solo una gobernanza responsable y accountable permitirá aprovechar sus beneficios mientras se respetan los derechos humanos. Área de estudio general: Inteligencia Artificial= . Área de estudio específica: Tecnología y prevención del crimen. Tipo de estudio: Revisión sistemática cualitativa

 

 

Keywords:

Facial recognition

Crime prevention

Ethics

Human rights

Surveillance

 

Abstract

Introduction: Facial recogni= tion is an artificial intelligence technology that allows automated identifica= tion of individuals from images or videos of their faces. Its use by police and security agencies to prevent crime has rapidly increased in recent years. However, this application raises significant ethical concerns related to privacy, protection of personal data, freedom of thought, presumption of innocence, and nondiscrimination. Objective: To evaluate the effectiveness and ethical implications of implementing facial recognition technologies in urban environments for crime prevention, providing evidence-based recommendations for responsible application. Methodolog= y: A qualitative systematic review of scientific literature published between 2017 and 2022 on the use of facial recognition in crime prevention was conducted. Sixteen articles meeting predefined eligibility criteria were analyzed, sourced from academic journals in artificial intelligence, law, criminology, and ethics. Results: Reviewed studies suggest that fa= cial recognition is increasingly adopted by security forces in public spaces. While algorithms have achieved high levels of accuracy, evidence of their effectiveness in reducing crime is still limited and mixed. At the same t= ime, significant ethical risks arise, including potential violations of fundamental rights, algorithmic biases, mass surveillance, and erosion of civil liberties. Experts advocate for strict regulations, impact assessme= nts, independent oversight, and public debate to prevent abuses. Conclusion= : Facial recognition represents a promising yet controversial tool for crime prevention. Its actual effectiveness remains uncertain, and its ethical r= isks are significant. A precautionary approach with robust legal and technical safeguards, constant monitoring, and broad social deliberation is require= d to balance security and freedom. Only responsible and accountable governance will allow harnessing its benefits while respecting human rights. Gene= ral Study Area: Artificial Intelligence. Specific Study Area: Tech= nology and Crime Prevention. Study Type: Qualitative Systematic Review.

 

 

 

Introducción<= /b>

El reconocimiento facial= es una tecnología de inteligencia artificial que permite identificar individuo= s de manera automatizada a partir de imágenes o videos de sus rostros. Funciona mediante la extracción y comparación de características faciales únicas, co= mo la distancia entre los ojos, la forma del mentón, etc.(1).

Estos sistemas generalme= nte constan de dos componentes principales: un algoritmo de procesamiento de imágenes y una o varias bases de datos con información facial pre-registrad= a, como fotos de pasaportes o licencias de conducción (2). El algoritmo extrae un "mapa" digital de las características faciales de una imagen capt= ada en vivo y lo compara con las imágenes almacenadas en la base de datos para determinar una coincidencia.

Existen diferentes modalidades de reconocimiento facial según el número de personas en las imágenes capturadas y en la base de datos (2): Verificación (1:1): compara la imagen capturada con una imagen pre-registrada para verificar una identidad. Identificación (1:N): compara la imagen con muchas imágenes en u= na base de datos para determinar una identidad. Vigilancia (N:M): analiza much= as imágenes en busca de muchas identidades, por ejemplo, de una lista de sospechosos.

Las aplicaciones del reconocimiento facial abarcan desde el desbloqueo de smartphones, hasta la identificación de criminales o personas desaparecidas. En el ámbito de la seguridad pública, su uso se ha expandido para la vigilancia en espacios públicos en combinación con circuitos cerrados de televisión (CCTV), con el objetivo de identificar amenazas en tiempo real (3).

El reconocimiento facial= ha ganado gran relevancia en los últimos años debido a sus diversas aplicacion= es, entre ellas la prevención del crimen. Esta herramienta permite detectar y reconocer rostros humanos de manera automatizada a partir de imágenes o vid= eos (2). Su uso se ha extendido= en el ámbito de la seguridad pública, siendo adoptada por departamentos de pol= icía en distintas partes del mundo para identificar sospechosos y personas con o= rden de arresto en tiempo real (4).

Lo que permite combatir = la delincuencia en las ciudades inteligentes, estos sistemas permiten la identificación de sospechosos y personas con orden de arresto en tiempo rea= l a partir de imágenes o videos capturados por cámaras de vigilancia ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemDa= ta":{"ISBN":"4249202100","author":[{&quo= t;dropping-particle":"","family":"Neil",= "given":"Shah","non-dropping-particle":"= ","parse-names":false,"suffix":""},{&quo= t;dropping-particle":"","family":"Nandish&quo= t;,"given":"Bhagat","non-dropping-particle":&= quot;","parse-names":false,"suffix":""},= {"dropping-particle":"","family":"Manan&= quot;,"given":"Shah","non-dropping-particle":= "","parse-names":false,"suffix":""}= ],"container-title":"Visual Computing for Industry, Biomedicine, and Art","id":"ITE= M-1","issue":"9","issued":{"date-pa= rts":[["2013"]]},"page":"1-14","pub= lisher":"Visual Computing for Industry, Biomedicine, and Art","title":"Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention","type":"article-journal","volume&= quot;:"7"},"uris":["http://www.mendeley.com/docume= nts/?uuid=3D7b121444-04ac-4b1e-822c-aa3b77402b9f"]}],"mendeley&qu= ot;:{"formattedCitation":"(5)","plainTextFormatted= Citation":"(5)","previouslyFormattedCitation":&quo= t;(5)"},"properties":{"noteIndex":0},"schema&= quot;:"https://github.com/citation-style-language/schema/raw/master/cs= l-citation.json"}(5). Sin embargo, algunas agencias van aún más allá, aprovechando también tecnologías de reconocimien= to de emociones para inferir los estados afectivos internos de las personas a partir de rasgos como los movimientos de los músculos faciales. De esta for= ma, se busca obtener pistas sobre individuos desconocidos que actúan de manera sospechosa en lugares públicos o estratégicos (6).

Aunque el uso de estas herramientas aún parece relegado a escenarios distópicos, ya es una realida= d en algunas partes del mundo. Por ejemplo, un artículo publicado por investigad= ores de la Academia de Policía de Hubei en China examina el valor de las expresi= ones faciales para identificar "personas peligrosas" y "grupos de alto riesgo" (7). Los autores proponen construir una base de datos de imágenes de video de delincuentes antes y después de cometer crímenes, para entrenar un algoritmo que reconozca a los individuos involucrados en actividades ilícitas.

Sin embargo, la compatibilidad de estas tecnologías con el marco europeo de derechos humano= s es cuestionada. Expertos señalan que el reconocimiento facial y emocional podr= ía vulnerar derechos fundamentales como la privacidad, la protección de datos personales, la libertad de pensamiento y la presunción de inocencia = (2). Además, persisten dudas sobre la efectividad y precisión de estos sistemas, existiendo el riesgo de criminalizar erróneamente a personas inocentes (8).

Por otro lado, algunos modelos basados en aprendizaje profundo están mostrando un desempeño promet= edor en el reconocimiento de emociones a partir de expresiones faciales. Un algoritmo híbrido que combina redes neuronales convolucionales (CNN) y patr= ones binarios locales (LBP) logró una precisión promedio del 88% en el reconocimiento de 6 emociones básicas (9). Estos avances brindan = una dirección para analizar características psicológicas a partir del reconocimiento de emociones.

El debate sobre el despliegue de tecnologías de reconocimiento facial para fines preventivos d= ebe considerar todos sus potenciales impactos, tanto positivos como negativos. = Es necesario un análisis riguroso y caso por caso que tome en cuenta no sólo l= os beneficios reportados, sino también los cuestionamientos sobre su compatibilidad con los derechos fundamentales. Mientras tanto, el desarroll= o de modelos de aprendizaje automático cada vez más precisos abre nuevas posibilidades para inferir estados psicológicos a partir de expresiones faciales, lo cual podría tener aplicaciones promisorias, pero también preocupantes.

La presente revisión bus= ca como fin evaluar la eficacia y las implicaciones éticas de la implementació= n de la tecnología de reconocimiento facial en entornos urbanos para la prevenci= ón del crimen, proporcionando recomendaciones fundamentadas para una aplicación responsable.

 

Metodología

La presente investigación se llevó a cabo bajo un enfoque cualitativo, emplean= do un diseño de revisión bibliográfica. Este diseño permite recopilar, evaluar críticamente y sintetizar la evidencia disponible sobre un tema específico = de manera metódica y reproducible (10). El estudio e= s de tipo básico, ya que busca generar conocimiento sobre el uso del reconocimie= nto facial para la prevención del crimen, sin una aplicación práctica inmediata= . El nivel de investigación es descriptivo, pues se propone caracterizar el esta= do actual de esta tecnología, sus aplicaciones y los desafíos éticos que plantea. La modalidad es documental, basada en la revisión de artículos científicos publicados.

Se aplicó el método analítico-sintético, que consiste en descomponer el objeto= de estudio en sus partes constitutivas para analizarlas de forma individual (análisis) y luego integrarlas para estudiarlas de manera holística e integ= ral (síntesis) (11). Esto permitió examinar en detalle los diferentes aspectos del reconocimiento facial en la prevención del delito abordados en cada artículo, para luego desarrollar una comprensión global del tema.

La revisión bibliográfica se realizó siguiendo estos pasos: 1) Formulación del problema y preguntas de investigación; 2) Definición de criterios de inclus= ión y exclusión de estudios; 3) Búsqueda y selección de artículos relevantes en bases de datos académicas; 4) Evaluación de la calidad metodológica de los estudios; 5) Extracción y síntesis de datos; 6) Interpretación de resultado= s y elaboración de conclusiones.

Se empleó la técnica de análisis documental, que permite recolectar datos de fuentes secundarias, como libros, boletines, revistas, folletos y periódicos (Bernal, 2010). Específicamente, se analizaron artículos de revistas científicas indexadas publicados en los últimos 5 años.

La población de estudio comprendió todos los artículos científicos sobre reconocimiento facial para prevención del crimen publicados en revistas indexadas entre 2020 y 2023. No se realizó muestreo, sino que se revisaron todos los artículos que cumplieron los siguientes criterios de inclusión: a= ) Artículos publicados en revistas científicas revisadas por pares; b) Estudios enfocad= os en aplicaciones de reconocimiento facial por parte de agencias policiales o= de seguridad; c) Investigaciones que aborden aspectos técnicos y/o éticos de e= sta tecnología; d) Textos completos disponibles en idioma inglés o español.

Se excluyeron artículos de opinión, revisiones narrativas, estudios enfocados = en aplicaciones comerciales del reconocimiento facial. Se eliminaron artículos duplicados o con calidad metodológica deficiente. En total, se analizaron 16 artículos que cumplieron los criterios de elegibilidad, provenientes de revistas de los campos de inteligencia artificial, derecho, criminología y ética.

No se requirió aprobación de un comité de ética ni consentimiento informado, ya que la investigación no involucró participantes humanos, sino que se basó e= n el análisis de documentos de acceso público. Se respetó la integridad de las i= deas de los autores y se citaron adecuadamente todas las fuentes consultadas, ev= itando cualquier forma de plagio.

Resultados y discusión

A partir de la revisión, se encontró que el reconocimiento facial está siendo crecientemente utilizado por agencias policiales y de seguridad en entornos urbanos para identificar sospechosos y prevenir delitos (5,7,12). Algunos sist= emas incluso incorporan análisis de emociones para detectar individuos potencialmente peligrosos a partir de sus expresiones faciales (2).

En cuanto a la eficacia, los estudios sugieren que los algoritmos de reconocimiento facial han alcanzado altos niveles de precisión, con tasas superiores al 90% en condiciones controladas (9,13). Esto ha permitido a las autoridades localizar y detener criminales buscados de mane= ra más rápida y eficiente (5). Asimismo, se argumenta que la sola presencia de estos sistemas tendría un efecto disuaso= rio sobre el crimen (12).

Sin embargo, la evidencia sobre su impacto real en las tasas de criminalidad es= aún limitada (1,6,7). Algunos estu= dios encuentran reducciones significativas en ciertos delitos donde se implementó esta tecnología (14), mientras otr= os no hallan efectos apreciables o incluso observan desplazamiento del crimen hacia áreas no vigiladas (2,15). Se necesitan evaluaciones más rigurosas y a mayor escala para llegar a conclusiones sóli= das sobre su efectividad.

Por otro lado, el uso del reconocimiento facial en espacios públicos genera importantes preocupaciones éticas. Diversos autores advierten sobre los rie= sgos para la privacidad, la protección de datos personales, la libertad de pensamiento, la presunción de inocencia y la no discriminación (2,4,7,12). El hecho de = que cualquier persona pueda ser identificada y rastreada sin su consentimiento mientras realiza actividades cotidianas resulta problemático. Más aún si se utiliza para inferir estados mentales o intenciones a partir de gestos faciales, lo cual es altamente especulativo (2).

Otro riesgo es que estos sistemas amplifiquen sesgos discriminatorios, ya que algunos algoritmos han mostrado mayores tasas de error para ciertos grupos demográficos, como mujeres y personas de piel oscura (12,16). También preo= cupa el potencial de función creep (La "function creep" es un término utilizado para describir el fenómeno en el que una tecnología, sistema o proceso diseñado para un propósito específico comienza a utilizarse de mane= ra más amplia o para funciones adicionales a las originalmente previstas), don= de datos recabados para un fin legítimo terminen siendo usados para otros propósitos más invasivos (13,17). Existe el te= mor de que se normalice una vigilancia masiva y ubicua que erosione los derechos civiles.

Ante esto, diversos expertos plantean la necesidad de establecer regulaciones estrictas y mecanismos de supervisión independientes para prevenir abusos <= /span>(12). El uso de reconocimiento facial por parte del Estado debería limitarse a casos graves donde exista necesidad y proporcionalidad, con autorización judicial, transparencia y oportunidad de apelación. También se sugiere prohibir el análisis de emociones y la interconexión con otras bases de datos sensibles= (2).

Otra recomendación es desarrollar estándares técnicos y auditorías externas que garanticen la precisión, confiabilidad y equidad de los sistemas antes de su despliegue (9). Es crucial realizar evaluaciones de impacto algorítmico que identifiquen y mitiguen posibles sesgos discriminatorios (13). Asimismo, se debe capacitar adecuadamente al personal policial para que opere esta tecnología de manera ética y rigurosa.

Tabla 1. Tendencias en el uso del reconocimi= ento facial para la prevención del crimen

Aspecto

Tendencia

Adopción por agencias de seguridad

En aumento, con mayor número de departamentos de policía y gobiernos locales implementando estos sistemas en espacios públicos.(5,7,12).

Capacidades técnicas

Mejora continua en precisión y eficiencia de algoritmos, alcanzando tasas de reconocimiento superiores al 90% en ciertos contextos.<= /p>

Incorporación de análisis de emociones para detección de potenciales amenazas.ADDIN CSL_CIT= ATION {"citationItems":[{"id":"ITEM-1","item= Data":{"ISBN":"9783031139529","author":[= {"dropping-particle":"","family":"Neroni= ","given":"Isadora","non-dropping-particle&qu= ot;:"","parse-names":false,"suffix":"&qu= ot;}],"container-title":"Department of Legal Studies","id":"ITEM-1","issued&quo= t;:{"date-parts":[["2022"]]},"page":"1-2= 3","title":"Facial Recognition for Preventive Purposes: The Human Rights Implications of Detecting Emotions in Public Spaces.","type":"article-journal"},"uris&qu= ot;:["http://www.mendeley.com/documents/?uuid=3D0c4986e7-f250-43f3-b83= d-6c391a47a958"]},{"id":"ITEM-2","itemData&qu= ot;:{"DOI":"10.1155/2022/2249417","ISSN":&quo= t;16875273","PMID":"36188698","abstract"= :"The exploration of facial emotion recognition aims to analyze psychological characteristics of juveniles involved in crimes and promote the applicati= on of deep learning to psychological feature extraction. First, the relation= ship between facial emotion recognition and psychological characteristics is discussed. On this basis, a facial emotion recognition model is construct= ed by increasing the layers of the convolutional neural network (CNN) and integrating CNN with several neural networks such as VGGNet, AlexNet, and LeNet-5. Second, based on the feature fusion, an optimized Central Local Binary Pattern (CLBP) algorithm is introduced into the CNN to construct a CNN-CLBP algorithm for facial emotion recognition. Finally, the validity analysis is conducted on the algorithm after the preprocessing of face im= ages and the optimization of relevant parameters. Compared with other methods,= the CNN-CLBP algorithm has higher accuracy in facial expression recognition, = with an average recognition rate of 88.16%. Besides, the recognition accuracy = of this algorithm is improved by image preprocessing and parameter optimizat= ion, and there is no poor-fitting. 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Efectividad en reducción del crimen

Resultados mixtos y poco concluyentes. Algunos estudios reportan disminución de deli= tos en áreas monitoreadas, mientras otros no encuentran impactos significativ= os o sugieren desplazamiento a zonas no vigiladas.(2,5,7)

Preocupaciones éticas

Creciente inquietud por potenciales vulneraciones a privacidad, protección de datos, libertad de pensamiento, presunción de inocencia y no discriminación.

Riesgos de sesgos algorítmicos, vigilancia masiva, function creep y erosión de derechos civiles. (2,9,12,13)

Regulación

Llamados a establecer marcos legales estrictos que restrinjan y supervisen el uso = de reconocimiento facial por agencias estatales.

Propuestas de prohibir análisis de emociones y cruzamiento con bases de datos sensib= les.

Demanda de estándares técnicos, auditorías externas y evaluaciones de impacto.(9,12,13,16)

Debate público

Creciente polarización de posturas entre defensores del orden y de los derechos civiles.

Necesidad de diálogo social amplio para sopesar costos y beneficios, y definir equilibrios entre seguridad pública y libertades individuales.(2,12)

La tabla 1 muestra un panorama de rápida expansión en la adopción y capacidades del reconocimiento facial para seguridad pública, pero con impactos aún inciertos en los niveles de criminalidad. Al mismo tiempo, surgen crecientes alarmas éticas que motivan llamados a una regulación más rigurosa y a un de= bate social informado. El desafío es encontrar un balance entre los beneficios y riesgos de esta poderosa tecnología.

 

Figura 1. Implicaciones éticas del reconocimi= ento facial

La figura 1 presenta de manera concisa las principales preocupaciones éticas asociadas con el reconocimiento facial en entornos urbanos, abordando áreas como la privacidad, protección de datos personales, libertad de pensamiento, presunción de inocencia y no discriminación. Propone acciones específicas, incluida la necesidad de regulación estricta y supervisión independiente, mientras enfatiza la importancia de encontrar un equilibrio entre la seguri= dad pública y las libertades individuales. Invita a la participación activa de = la audiencia para discutir y reflexionar sobre el tema, promoviendo un enfoque reflexivo y colaborativo hacia la resolución de estas complejas cuestiones éticas.

Si bien el reconocimiento facial representa una herramienta prometedora para combatir la delincuencia, su eficacia aún no ha sido fehacientemente comprobada. Al mismo tiempo, entraña serios riesgos para los derechos fundamentales que no pueden ser ignorados. Se requiere un debate social amp= lio y mecanismos de gobernanza robustos para sopesar costos y beneficios. Sólo = así podrá alcanzarse un equilibrio adecuado entre seguridad y libertad en la er= a de la vigilancia biométrica.

Conclusión

·      =    A partir del análisis realizado, se pu= ede concluir que, si bien estos sistemas han demostrado un gran potencial para asistir a las fuerzas de seguridad en la identificación y detención de sospechosos, su eficacia real en la reducción de las tasas de criminalidad = aún no ha sido concluyentemente probada. Se necesitan evaluaciones más rigurosa= s y a mayor escala para determinar su impacto neto, considerando posibles efect= os de desplazamiento del delito.

·      =    Además, la implementación de reconocimiento facial en espacios públicos plantea serias preocupaciones ét= icas que no pueden ser ignoradas. Existe el riesgo de vulnerar derechos fundamentales como la privacidad, la protección de datos personales, la libertad de pensamiento, la presunción de inocencia y la no discriminación.= La posibilidad de una vigilancia masiva y ubicua, potencialmente sesgada y desproporcionada, genera alarmas justificadas. El análisis automatizado de emociones con fines preventivos resulta especialmente cuestionable dado su carácter especulativo y potencial invasividad.

·      =    Por lo tanto, se recomienda que cualqu= ier despliegue de esta tecnología por parte de agencias estatales se realice ba= jo un marco legal estricto que garantice su necesidad, proporcionalidad, transparencia y supervisión independiente. Su uso debería limitarse a la investigación de delitos graves, con autorización judicial y acceso a mecanismos de apelación. Es crucial establecer salvaguardas contra la interconexión con otras bases de datos sensibles y el function creep.<= /o:p>

·      =    Asimismo, se sugiere el desarrollo de estándares técnicos y la realización de auditorías externas para asegurar l= a precisión, confiabilidad y equidad de los sistemas de reconocimiento facial antes de su adopción. Las evaluaciones de impacto algorítmico deben ser obligatorias pa= ra identificar y mitigar potenciales sesgos discriminatorios. El personal poli= cial a cargo de operar esta tecnología debe recibir capacitación adecuada en su = uso ético y riguroso.

·      =    Más allá de los resguardos legales y técnicos, se requiere un amplio debate social para definir los equilibrios apropiados entre seguridad pública y libertades individuales en la era de la vigilancia biométrica. Los costos y beneficios deben ser cuidadosamente sopesados con la participación de todos los actores relevantes. Solo a trav= és del diálogo democrático se podrán construir los consensos necesarios para u= na gobernanza legitima y sustentable del reconocimiento facial.

·      =    Finalmente, el reconocimiento facial representa una herramienta prometedora pero también riesgosa en el campo de= la prevención del delito. Su eficacia aún no es concluyente y sus implicancias éticas son profundas. Un enfoque de precaución, con regulaciones robustas, evaluaciones rigurosas, supervisión constante y deliberación pública, resul= ta esencial para aprovechar sus beneficios potenciales mientras se previenen vulneraciones a derechos humanos. El objetivo final debe ser alcanzar la me= jor combinación posible de seguridad y libertad a través de una aplicación responsable y sometida a rendición de cuentas de esta poderosa tecnología.<= o:p>

 

 

Conflicto de intereses=

No existen conflictos de intereses en relación con el artículo presentado.

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El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento = de la Revista Anatomía Digital.<= /o:p>

 


=

 

 

 

El artículo qued= a en propiedad de la revista y, por tanto, su publicación parcial y/o total en o= tro medio tiene que ser autorizado por el director de la Revista Anatomía Digital.

 

 


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ISSN: 2697-3391

Vol. 7 No. 2.2, pp. 209 – 221, julio 2024
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                                      Ciencias Forenses                Página 209 | 221

 

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