Training > AI/Machine Learning > Ethics in AI and Data Science (LFS112x)
Training Course

Ethics in AI and Data Science (LFS112x)

Learn how to build and incorporate ethical principles and frameworks in your AI and Data Science technology and business initiatives to add transparency, build trust, drive adoption, and lead with responsibility.

Who Is It For

The course is designed for business, government and technology leaders and data scientists who are responsible for building and adopting AI tools.
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What You’ll Learn

In this course you will learn about business drivers for AI, the ethical challenges and impacts of AI and Data Science, the business and societal dynamics at work in an AI world, the key principles for building responsible AI, and more. This course introduces some of the principles and frameworks that puts ethics and responsibility into practice in the data analytics profession. And offers practical approaches to technical, business and leadership dilemmas and challenges posed by work in AI and Data Science.
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What It Prepares You For

You will walk away from this course with an understanding of how to add transparency, develop standards and share best practices to build trust and drive AI adoption.
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Course Outline
Welcome
Chapter 1. What’s at Stake? The Need for Ethics, Trust & Responsibility
Chapter 2. What do we mean by Artificial Intelligence?
Chapter 3. The Complex World of Data
Chapter 4. The Challenges & Strategies for Putting Ethics into Practice
Final Exam (Verified track only)

Prerequisites
Reviews
Feb 2022
I was very happy that most of the content was presented as nuanced text. For a topic this complex and dynamic, it makes the most sense. The links and references included in the text were amazingly educational, and they perfectly complemented the exposed concepts.
Feb 2022
I really liked the ethics of AI, in that he wasn't just thinking about the extent of our work, but our ability to take responsibility for it, developing trust by means of accountability.
Feb 2022
I liked that the content was clear and well organized. I particularly liked that the focus of the course was on open and adaptable principles, instead of on specific technological issues. The provided introduction to AI is necessary to better understand the specific cases and examples discussed throughout the course, but the technological aspects never obfuscate the main subject of the course.
Feb 2022
The course address a lot of topics, challenges and advice about ethics and responsibility in AI and Data Science that often are ignored, but whose importance is high. I liked all the external references which gave me a broader context of the topics, and were useful to extend the ideas and the learning itself.
Oct 2021
The ethics of AI and ML are something that I hadn't really thought much about. The depth and the ability to show the interconnectedness of ethics and AI/ML was insightful.