Guide for public and private entities on Transparency and Personal Data Protection for a responsible Artificial Intelligence
Guía para entidades públicas y privadas en materia de Transparencia y Protección de Datos Personales para una Inteligencia Artificial responsable
This guide provides recommendations for public and private entities in Argentina on transparency and personal data protection for responsible artificial intelligence. Developed by the Agency for Access to Public Information (AAIP), it outlines principles, potential risks such as bias and privacy violations, and recommendations across the four stages of the AI lifecycle: design, verification and validation, implementation, and operation and maintenance.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This guide is for public and private organizations, developers, deployers, and researchers involved in creating or implementing artificial intelligence systems in Argentina.
What the document contains
The document covers definitions of artificial intelligence and automated decision systems, associated risks like bias and privacy breaches, key data protection and transparency principles, and actionable recommendations categorized across the four stages of the AI lifecycle: design, verification and validation, implementation, and operation and maintenance.
It does not provide binding statutory penalties or sector-specific technical compliance rules beyond general public administrative and data protection guidelines.
- The guide establishes recommendations for integrating transparency and personal data protection into AI projects throughout their entire lifecycle.
- It emphasizes proactive responsibility, privacy by design and by default, and robust impact assessments to mitigate risks like algorithmic bias and discrimination.
- Organizations utilizing automated decision-making systems must ensure algorithmic transparency and provide clear mechanisms for user communication and redress.
- The document highlights specific data protection challenges related to sensitive data, biometric data, and neurodata.
- It aligns with international standards, referencing frameworks such as the UNESCO Recommendation on the Ethics of AI and the Council of Europe's Convention 108+.
· Introducción · Objetivo · Alcance · Definiciones y características de los sistemas de Inteligencia Artificial · Principales problemas y desafíos de la IA · Principios de Transparencia y Protección de Datos Personales · Recomendaciones de Transparencia y Protección de Datos Personales en el ciclo de vida de Sistemas de IA · Consideraciones finales · Acrónimos · Referencias bibliográficas
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
4/5Clear responsibility for AI outcomes
El principio de responsabilidad proactiva y demostrada se ha transformado en los últimos años en un pilar fundamental en la protección de datos personales
Etapa 1: Diseño de sistema
screening score · 2026-10-23
Human Dignity
3/5Respect for the inherent worth of persons
La preservación de la intimidad y la protección de los datos personales constituyen derechos esenciales para la protección de la dignidad
Principios de Transparencia y Protección de Datos Personales
screening score · 2026-10-23
Privacy
4/5Protection of personal data and private life
La IA requiere en la etapa de programación grandes cantidades de datos, entre los cuales pueden incluirse datos personales. Una mala gestión de estos datos puede llevar a violaciones de la privacidad
Introducción
screening score · 2026-10-23
Transparency
4/5Openness about how AI systems work and are used
A su vez, se debe promover la transparencia algorítmica a través de la adopción de medidas para garantizar condiciones adecuadas de explicabilidad de los sistemas
Etapa 1: Diseño de sistema
screening score · 2026-10-23