Responsible AI: Fairness and Bias in AI Development
Learn to implement responsible AI practices, focusing on fairness and bias mitigation in AI/ML systems using Google Cloud tools.
Learn to implement responsible AI practices, focusing on fairness and bias mitigation in AI/ML systems using Google Cloud tools.
This course introduces developers to responsible AI concepts, focusing on fairness and bias in AI systems. Learn Google's AI principles, practical techniques for identifying and mitigating bias, and how to implement best practices using Google Cloud products and open-source tools. Gain hands-on experience with tools like TFDV, What-If Tool, and TFMA to ensure more equitable AI development.
Instructors:
English
Español
Powered by
What you'll learn
Define responsible AI and its importance in modern technology development
Identify and explain Google's AI principles
Understand fairness and bias in the context of AI systems
Use tools like TFDV, What-If Tool, and TFMA to detect bias in AI models
Apply bias mitigation techniques including data intervention and model correction
Implement MinDiff in TensorFlow for bias mitigation
Skills you'll gain
This course includes:
1.18 Hours PreRecorded video
2 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 5 modules in this course
This course provides a comprehensive introduction to responsible AI practices for developers, with a focus on fairness and bias in AI systems. Students will learn about Google's AI principles, techniques for identifying and mitigating bias in data and models, and practical tools for implementing responsible AI. The course covers key concepts such as fairness metrics, bias detection using tools like TFDV and What-If Tool, and bias mitigation strategies including data intervention, threshold calibration, and model correction. Participants will gain hands-on experience with Google Cloud products and open-source tools, preparing them to develop more ethical and equitable AI systems.
Introducción al curso
Module 1 · 0 Minutes to complete
Introducción a la IA responsable
Module 2 · 35 Minutes to complete
Equidad y sesgos de la IA
Module 3 · 2 Hours to complete
Resumen del curso
Module 4 · 12 Minutes to complete
Recursos del curso
Module 5 · 40 Minutes to complete
Fee Structure
Payment options
Financial Aid
Instructor
Empowering Businesses with Expert Training from Google Cloud
The Google Cloud Training team is tasked with developing, delivering, and evaluating training programs that enable our enterprise customers and partners to effectively utilize our products and solutions. Google Cloud empowers millions of organizations to enhance employee capabilities, improve customer service, and innovate for the future using cutting-edge technology built specifically for the cloud. Our products are designed with a focus on security, reliability, and scalability, covering everything from infrastructure to applications, devices, and hardware. Our dedicated teams are committed to helping customers successfully leverage our technologies to drive their success.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.