Welcome to "Responsible AI – Principles and Ethical Considerations"! Dive deep into the very essence of Responsible AI with us. Uncover the significance of key principles shaping technology's future. From ethical considerations to fairness, transparency, and accountability, we discuss these principles with real-world examples, putting them into the context of data science.
Responsible AI - Principles and Ethical Considerations
Responsible AI - Principles and Ethical Considerations
This course is part of Leadership Strategies for AI and Generative AI Specialization
Instructors: Fractal Analytics
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4.4
(15 reviews)
Intermediate level
Recommended experience
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Discuss responsible AI principles and their significance in technology, including ethical considerations, fairness, transparency, and accountability.
Apply techniques to identify, address, and mitigate bias in AI algorithms and data, promoting fairness and inclusivity in AI systems.
Interpret and explain AI decisions, balancing accuracy and explainability to foster trust and accountability in AI systems.
Discuss accountability, ethical AI governance, privacy considerations, security measures in the development & deployment of responsible AI systems.
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Assessments
12 assignments
Taught in English
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This course is part of the Leadership Strategies for AI and Generative AI Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
In this module, you will learn about AI and the challenges it brings in different domains. You will be able to understand the need of Responsible AI and 6 principles of Responsible AI.
What's included
10 videos4 readings3 assignments2 discussion prompts1 plugin
In this module, you'll learn the concept of fairness within AI and gain insights into the different forms of biases that can infiltrate the machine learning pipeline. You will also learn about effective techniques for bias mitigation and measurement.
What's included
8 videos3 assignments1 discussion prompt
In this module, you will explore the concept of transparency in AI, gaining a deep understanding of its importance. You'll also discover how transparency in data and models plays a crucial role in achieving explainability, ultimately leading to transparent and explainable business decisions.
What's included
5 videos2 assignments
In this module, you'll learn the core concept of accountability in AI and its significance. Explore the concept of drift, including its various types, and delve into the diverse techniques for detecting drift in AI systems.
What's included
3 videos2 readings2 assignments1 discussion prompt
In this module, you'll learn the crucial need for data privacy in AI. Explore Privacy by Design, its foundational elements, and how it safeguards privacy in AI systems. Understand AI security and the concept of differential privacy for robust and private AI applications.
What's included
4 videos2 readings2 assignments
Instructors
Instructor ratings
3.6 (6 ratings)Offered by
Recommended if you're interested in Data Analysis
University of California, Davis
Coursera Instructor Network
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4.4
15 reviews
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RA
5
Reviewed on May 31, 2024
Excellent course content. Additional Hands on activities or projects would have been nice. Thanks for the course.
DA
5
Reviewed on Feb 22, 2024
This Coursera course on responsible AI is foundational and incredibly valuable for grasping the essential concepts of ethical considerations, fairness, transparency, and accountability in AI.
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