Data-driven technologies like AI, when designed with ethics in mind, benefit both the business and society at large. But it’s not enough to say you will “be ethical” and expect it to happen. We need tools and techniques to help us assess gaps in our ethical behaviors and to identify and stop threats to our ethical goals. We also need to know where and how to improve our ethical processes across development lifecycles. What we need is a way to manage ethical risk. This third course in the Certified Ethical Emerging Technologist (CEET) professional certificate is designed for learners seeking to detect and mitigate ethical risks in the design, development, and deployment of data-driven technologies. Students will learn the fundamentals of ethical risk analysis, sources of risk, and how to manage different types of risk. Throughout the course, learners will learn strategies for identifying and mitigating risks.
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Detect and Mitigate Ethical Risks
This course is part of CertNexus Certified Ethical Emerging Technologist Professional Certificate
Instructors: Renée Cummings
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What you'll learn
Summarize common sources of ethical risks.
Detect and mitigate ethical risks.
Evaluate risk identification and mitigation strategies within the lifecycle.
Analyze a sample AI model and create a plan to mitigate any identified risks.
Skills you'll gain
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There are 7 modules in this course
The first module in the course lays the groundwork for some concepts that are fundamental to data-driven technologies like artificial intelligence (AI). As an ethicist, you may not be putting these concepts into practice yourself, but you still need to understand them. That way, you'll be able to make more informed judgments and communicate with other people about how best to detect and mitigate ethical risks.
What's included
19 videos4 readings1 assignment1 discussion prompt
This module begins a series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. First, you'll learn more about the risks to users' privacy and private data.
What's included
18 videos3 readings1 assignment1 discussion prompt
This module continues the series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. Now, you'll tackle the risks to the organization's accountability.
What's included
17 videos1 reading1 assignment1 peer review1 discussion prompt
This is the next module in the ongoing series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. Next up are the related concepts of transparency and explainability.
What's included
16 videos2 readings1 assignment1 discussion prompt1 ungraded lab
This is the penultimate module in the ongoing series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. Here, you'll focus on managing risks to fairness and non-discrimination (bias).
What's included
18 videos3 readings1 assignment1 discussion prompt
This is the final module in the series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. Lastly, you'll address risks to both safety and security.
What's included
23 videos1 reading1 assignment1 discussion prompt
You'll work on one or more projects in which you'll apply your knowledge of the material in this course to practical scenarios.
What's included
2 peer reviews
Instructors
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Recommended if you're interested in Machine Learning
DeepLearning.AI
University of California, Santa Cruz
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