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Google Cloud Training

En este curso, se presentan los conceptos de la IA responsable y los principios de la IA. Se abordan técnicas para identificar de forma práctica la equidad y los sesgos, y mitigar los sesgos en las prácticas de IA/AA. Se exploran métodos y herramientas funcionales para implementar prácticas recomendadas de la IA responsable con productos de Google Cloud y herramientas de código abierto.

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What's inside

Syllabus

Introducción al curso
En este módulo, se presenta la estructura del curso y sus objetivos.
Introducción a la IA responsable
En este módulo, se ofrece una descripción general de la IA responsable y se abordan los principios de la IA de Google y temas secundarios de la IA responsable. Además, se brindan casos de éxito reales de la IA responsable en productos de Google.
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Equidad y sesgos de la IA
Este módulo se enfoca en la equidad y los sesgos de la IA. Proporciona varias técnicas y herramientas para identificar y mitigar sesgos a través de datos y modelado.
Resumen del curso
En este módulo, se proporciona un resumen de todo el curso que cubre los conceptos, las herramientas y las tecnologías más importantes.
Recursos del curso
Vínculos a archivos PDF de todos los módulos para estudiantes

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Introduces concepts of responsible AI and AI principles
Covers techniques for identifying and mitigating bias in AI/ML practices
Provides functional methods and tools for implementing responsible AI best practices
Emphasizes equity and bias in AI, a topic of growing importance
Outlines real-world examples of responsible AI in Google products

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Responsible AI for Developers: Fairness & Bias - Español with these activities:
Rehash the basics of data ethics and ethics in AI
Solidify your knowledge of the principles and concepts of responsible AI and data ethics.
Browse courses on Data Ethics
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  • Review materials from previous courses, workshops, seminars, or books on responsible AI and data ethics.
  • Explore case studies and industry use cases of responsible AI implementation.
  • Engage in online discussions or forums on topics related to ethical AI and data ethics.
Gather and organize resources on responsible AI best practices and tools
Build a valuable repository of resources to support your continued learning and application of responsible AI principles.
Browse courses on AI Best Practices
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  • Search for and curate articles, research papers, guidelines, and tools related to responsible AI.
  • Organize the resources into a structured format, such as a knowledge base or online repository.
  • Share your compilation with peers or the broader AI community.
Contribute to open-source projects related to responsible AI and data ethics
Join a community of developers and researchers working towards the advancement of responsible AI.
Show steps
  • Identify open-source projects focused on responsible AI, data ethics, or related topics.
  • Review the project documentation and codebase to understand its goals and technical requirements.
  • Contribute code, documentation, or other resources to the project.
Three other activities
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Practice identifying and mitigating bias in AI systems
Develop hands-on experience in identifying and mitigating bias throughout the AI/ML lifecycle.
Browse courses on Bias in AI
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  • Follow guided tutorials on bias detection and mitigation techniques.
  • Apply bias detection tools and methodologies to real-world AI/ML projects.
  • Document and share your findings and learnings with peers or mentors.
Volunteer with organizations promoting responsible AI and data ethics
Engage with real-world initiatives and contribute to the advancement of responsible AI practices in society.
Show steps
  • Identify organizations working in the field of responsible AI and data ethics.
  • Reach out to these organizations and inquire about volunteer opportunities.
  • Participate in projects or initiatives that align with your interests and skills.
Develop a plan for implementing responsible AI practices in a specific industry or domain
Apply your understanding of responsible AI to real-world scenarios and develop practical implementation plans.
Browse courses on AI Governance
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  • Research and analyze the specific industry or domain's AI landscape and ethical considerations.
  • Identify potential risks and challenges associated with AI implementation in that context.
  • Develop a comprehensive plan outlining responsible AI practices, governance mechanisms, and stakeholder engagement strategies.
  • Present your plan to peers, mentors, or industry experts for feedback and refinement.

Career center

Learners who complete Responsible AI for Developers: Fairness & Bias - Español will develop knowledge and skills that may be useful to these careers:

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