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CURSO : DERECHO E INTELIGENCIA ARTIFICIAL
TRADUCCION : LAW AND ARTIFICIAL INTELLIGENCE
SIGLA : DDE3153
CREDITOS : 10 UC / 6 SCT
MODULOS : 1 MÓDULO A LA SEMANA
REQUISITOS : SIN REQUISITOS
CONECTOR : NO APLICA
RESTRICCIONES : ALUMNOS DE DOCTORADO
CARACTER : OPTATIVO
TIPO : CATEDRA
CALIFICACION : ESTANDAR
NIVEL FORMATIVO : DOCTORADO


I.DESCRIPCIÓN DEL CURSO

Este curso busca exponer a los doctorandos a los problemas juridicos que los agentes artificiales generan entregandoles las herramientas tecnicas y juridicas que les permitan iniciar una investigacion autonoma sobre las problematicas emergentes en esta materia.


II.RESULTADOS DE APRENDIZAJE

General:
Analizar los diversos problemas juridicos que la inteligencia artificial supone para el Derecho.

En este contexto, sus objetivos especificos son:
1.Comprender el funcionamiento de los diversos sistemas de inteligencia artificial, desde los netamente simbolicos hasta aquellos de aprendizaje automatizado, con especial enfasis en redes neuronales, aprendizaje por refuerzo y aprendizaje no supervisado.
2.Comprender los diversos problemas juridicos que generan, desde sesgos y opacidad, hasta titularidad, responsabilidad y propiedad intelectual.
3.El analisis de los diversos problemas juridicos generados mediante herramientas dogmaticas, historicas y comparadas.


III.CONTENIDOS

-Historia de la inteligencia artificial
-Modelos de inteligencia artificial, simbolicos y conectivistas.
-Mecanismos de aprendizaje automatizado
-Posibilidades de futuro, inteligencia general y consciencia.
-Sesgos y sus causas.
-Responsabilidad por los actos de agentes artificiales.
-Propiedad intelectual e inteligencia artificial


IV.ESTRATEGIAS METODOLOGICAS

Se evaluara a traves de un trabajo escrito con forma de articulo de revista que se realizara en dos entregas, una primera en forma de borrador que el profesor corregira y devolvera a los alumnos a fin que realicen los cambios se?alados y una segunda y final entrega en una fecha posterior.


V.ESTRATEGIAS EVALUATIVAS

-Las estrategias evaluativas estan orientadas a demostrar el desarrollo de las habilidades propuestas en los resultados de aprendizaje del curso.
-Listar brevemente las diferentes estrategias de evaluacion que se consideraran a lo largo del curso y su porcentaje de ponderacion en la nota final.
-Se debe verificar coherencia con los resultados de aprendizaje y la metodologia del curso.
-Entre las evaluaciones se podria considerar: articulo, aplicacion o ejercicio real, analisis de casos, control, debate, estudio de casos, ensayo, exposicion, fichas de lecturas, informes, juego de roles, mapa mental, mapa conceptual, pruebas, portafolio, proyecto, propuestas, producciones creativas, presentacion oral, poster, reportes, simulaciones, seminarios, videos, entre otros.
-Se sugiere dar mayor ponderacion a aquella/s evaluacion/es que permiten evidenciar el logro de los aprendizajes mas relevantes que tiene el curso.
-Si bien las estrategias de evaluacion y su ponderacion deben indicarse en el programa oficial del curso, podran ser modificadas por el docente a cargo e informadas a los estudiantes al inicio del curso.

Ejemplo:
-Exposicion grupal : 30%
-Informe escrito : 30%
-Examen final escrito : 40%


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