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
Taught in English
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.
Details to know
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January 2024
Assessments
12 assignments
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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
Instructor
Offered by
Recommended if you're interested in Data Analysis
Fractal Analytics
Fred Hutchinson Cancer Center
Coursera Project Network
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