IA Responsable: Transparencia y Ética

Resumen

Did you know that while 75% of business leaders agree AI ethics is important, most admit they lack the necessary tools or frameworks to implement it? According to Datamation, the majority of companies recognize the significance of AI ethics but struggle with practical implementation.

The gap between knowing and doing is massive—and that’s where this course comes in. Responsible AI isn’t just about feeling ethical.

It’s about building systems that are safer, smarter, and more transparent from the ground up This course is designed for professionals who are shaping the future of artificial intelligence. It’s ideal for data scientists, machine learning engineers, AI project managers, product leads, compliance officers, policy advisors, and ethics reviewers.

Whether you're developing AI systems or ensuring they meet ethical and regulatory standards, this course equips you with the tools and knowledge to build responsible, unbiased AI applications. To get the most from this course, learners should have a basic understanding of machine learning workflows and the AI lifecycle.

Familiarity with general technology concepts and the ability to prompt tools like ChatGPT will be helpful. While prior experience with Python or Jupyter Notebooks is beneficial, it’s not mandatory—this course is built to be accessible and practical.

By the end of the course, learners will be able to identify and mitigate bias in AI systems, implement explainability tools like SHAP and LIME, and develop responsible AI checklists based on fairness and transparency. They will also learn to evaluate AI projects against compliance frameworks such as the NIST AI Risk Management Framework, ensuring that their systems are ethical, explainable, and aligned with industry standards.

Programa

• AI Responsable: Transparencia y Ética
En este curso, explorarás herramientas y marcos prácticos para construir sistemas de IA éticos y confiables. A través de la experiencia práctica con herramientas como AI Fairness 360, SHAP y LIME, aprenderás a auditar modelos para detectar sesgos, aumentar la explicabilidad e integrar la transparencia y el cumplimiento en tu flujo de trabajo. También desarrollarás y aplicarás listas de verificación de IA responsable, asegurando que tus proyectos de IA se alineen con estándares éticos y regulatorios sin comprometer la innovación.

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Impartido por

Brian Newman and Starweaver Instructor Team

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Materias

Computer Science