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Google Cloud Training
Neste curso, apresentamos conceitos de IA responsável e princípios de IA. Ele contém técnicas para identificar e reduzir o viés e aplicar a imparcialidade nas práticas de ML/IA. Vamos abordar ferramentas e métodos práticos para implementar as práticas recomendadas de IA responsável usando produtos do Google Cloud e ferramentas de código aberto.
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What's inside
Syllabus
Introdução ao curso
Neste módulo, você vai conhecer a estrutura e os objetivos do curso.
Introdução à IA responsável
Neste módulo, teremos uma visão geral da IA responsável e vamos abordar os subtópicos desse tema e os princípios de IA do Google. Também veremos casos de estudo reais da IA responsável nos produtos do Google.
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Syllabus
Introdução ao curso
Neste módulo, você vai conhecer a estrutura e os objetivos do curso.
Introdução à IA responsável
Neste módulo, teremos uma visão geral da IA responsável e vamos abordar os subtópicos desse tema e os princípios de IA do Google. Também veremos casos de estudo reais da IA responsável nos produtos do Google.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Fornece uma base sólida para iniciantes no campo de IA responsável
Traz estudos de caso da própria Google para ilustrar princípios da IA responsável
Inclui um módulo para resumir os principais conceitos, ferramentas e tecnologias abordadas
Não menciona explicitamente os pré-requisitos necessários
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Reviews summary
Ia responsável para desenvolvedores
De acordo com os alunos, este curso oferece uma excelente introdução à IA responsável, com foco em identificação e mitigação de viés e aplicação da imparcialidade. Muitos elogiam a clareza dos conceitos e a relevância do conteúdo para o cenário atual. O curso se destaca por apresentar exemplos práticos e o uso de ferramentas do Google Cloud e código aberto. No entanto, alguns desenvolvedores mais experientes mencionam que a profundidade técnica poderia ser maior, sugerindo que é mais adequado para quem busca uma base sólida do que desafios complexos.
Conteúdo atual e essencial para IA.
"Muito relevante para o cenário atual da IA."
"Conteúdo essencial para quem trabalha com IA."
"Ajudou-me a pensar criticamente sobre as implicações éticas dos modelos de ML."
Aborda o uso de ferramentas e casos reais.
"Os exemplos práticos com ferramentas do Google Cloud são muito úteis."
"Cobriu a base de IA responsável e viés de forma acessível. A parte sobre ferramentas de código aberto foi um diferencial..."
"Os casos de estudo do Google foram muito elucidativos. Ajudou-me a pensar criticamente sobre as implicações éticas dos modelos de ML."
Proporciona uma base sólida em IA responsável.
"Excelente curso! Ajudou-me a entender profundamente os conceitos de IA responsável e como identificar e mitigar viés."
"Muito relevante para o cenário atual da IA. Os módulos sobre imparcialidade e viés são claros e bem estruturados."
"Consegui aplicar alguns conceitos no meu trabalho imediatamente. Ótimo trabalho!"
Bom para iniciantes, menos para experientes.
"Para desenvolvedores mais experientes, a profundidade técnica em algumas ferramentas poderia ser maior. Senti falta de desafios mais complexos."
"Achei que o curso prometia mais do que entregou em termos de profundidade técnica. Os conceitos são importantes, mas as soluções práticas foram apresentadas de forma muito básica."
"Entendi os conceitos, mas não me sinto totalmente preparado para implementar sozinho. Talvez precise de um curso complementar mais hands-on."
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 - Português with these
activities:
Revise the principles of artificial intelligence
Show steps
Familiarize yourself with the fundamental concepts of AI, including its history, different types, and applications, to enhance your understanding of the course materials.
Browse courses on
Artificial Intelligence
Show steps
-
Read introductory articles or books on AI
-
Watch video tutorials or online lectures on AI concepts
-
Review your notes or materials from previous courses related to AI
Compile resources on responsible AI practices
Show steps
Enhance your knowledge and stay up-to-date by collecting and organizing relevant resources, such as articles, whitepapers, and industry reports, that provide insights into responsible AI practices.
Browse courses on
Responsible AI
Show steps
-
Identify credible sources of information on responsible AI
-
Gather and review resources on AI ethics, best practices, and guidelines
-
Categorize and organize the resources for easy access
Attend industry events and conferences on responsible AI
Show steps
Engage with professionals in the field of responsible AI by attending industry events and conferences, allowing you to learn about the latest developments and connect with experts in the field.
Browse courses on
Responsible AI
Show steps
-
Identify relevant industry events and conferences
-
Prepare for the event by researching speakers and topics
-
Actively participate in discussions and networking sessions
Three other activities
Expand to see all activities and additional details
Show all six activities
Explore case studies of responsible AI implementations
Show steps
Gain practical insights into real-world applications of responsible AI by examining case studies that demonstrate how organizations have implemented AI with ethical considerations.
Browse courses on
Responsible AI
Show steps
-
Identify case studies of responsible AI implementations
-
Analyze the approaches and techniques used to ensure fairness and mitigate bias
-
Evaluate the outcomes and impact of these responsible AI implementations
Identify and mitigate bias in AI models
Show steps
Strengthen your understanding of bias in AI models by practicing techniques for identifying and mitigating bias using data analysis and model evaluation methods.
Browse courses on
AI Bias
Show steps
-
Review different types of bias in AI models
-
Analyze datasets for potential sources of bias
-
Apply bias mitigation techniques such as data sampling and算法调整
-
Evaluate the effectiveness of bias mitigation strategies
Develop an AI solution with responsible AI principles
Show steps
Apply the principles of responsible AI to design and develop an AI solution that addresses real-world problems while considering ethical and societal implications.
Browse courses on
Responsible AI
Show steps
-
Define the problem and identify the AI solution
-
Gather and prepare data responsibly
-
Develop the AI model with bias mitigation techniques
-
Evaluate and validate the AI solution for fairness and accuracy
-
Deploy the AI solution and monitor its impact
Revise the principles of artificial intelligence
Show steps
Familiarize yourself with the fundamental concepts of AI, including its history, different types, and applications, to enhance your understanding of the course materials.
Browse courses on
Artificial Intelligence
Show steps
Show steps
Show steps
- Read introductory articles or books on AI
- Watch video tutorials or online lectures on AI concepts
- Review your notes or materials from previous courses related to AI
Compile resources on responsible AI practices
Show steps
Enhance your knowledge and stay up-to-date by collecting and organizing relevant resources, such as articles, whitepapers, and industry reports, that provide insights into responsible AI practices.
Browse courses on
Responsible AI
Show steps
Show steps
Show steps
- Identify credible sources of information on responsible AI
- Gather and review resources on AI ethics, best practices, and guidelines
- Categorize and organize the resources for easy access
Attend industry events and conferences on responsible AI
Show steps
Engage with professionals in the field of responsible AI by attending industry events and conferences, allowing you to learn about the latest developments and connect with experts in the field.
Browse courses on
Responsible AI
Show steps
Show steps
Show steps
- Identify relevant industry events and conferences
- Prepare for the event by researching speakers and topics
- Actively participate in discussions and networking sessions
Three other activities
Expand to see all activities and additional details
Show all six activities
Explore case studies of responsible AI implementations
Show steps
Gain practical insights into real-world applications of responsible AI by examining case studies that demonstrate how organizations have implemented AI with ethical considerations.
Browse courses on
Responsible AI
Show steps
Show steps
Show steps
- Identify case studies of responsible AI implementations
- Analyze the approaches and techniques used to ensure fairness and mitigate bias
- Evaluate the outcomes and impact of these responsible AI implementations
Identify and mitigate bias in AI models
Show steps
Strengthen your understanding of bias in AI models by practicing techniques for identifying and mitigating bias using data analysis and model evaluation methods.
Browse courses on
AI Bias
Show steps
Show steps
Show steps
- Review different types of bias in AI models
- Analyze datasets for potential sources of bias
- Apply bias mitigation techniques such as data sampling and算法调整
- Evaluate the effectiveness of bias mitigation strategies
Develop an AI solution with responsible AI principles
Show steps
Apply the principles of responsible AI to design and develop an AI solution that addresses real-world problems while considering ethical and societal implications.
Browse courses on
Responsible AI
Show steps
Show steps
Show steps
- Define the problem and identify the AI solution
- Gather and prepare data responsibly
- Develop the AI model with bias mitigation techniques
- Evaluate and validate the AI solution for fairness and accuracy
- Deploy the AI solution and monitor its impact
Career center
Learners who complete Responsible AI for Developers: Fairness & Bias - Português will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/a5fi85/responsible
Reading list
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