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Artificial Intelligence Privacy and Convenience

This course is part of Ethics in the Age of AI Specialization

Instructor: LearnQuest Network

9,200 already enrolled

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3 modules

Gain insight into a topic and learn the fundamentals.

108 reviews

Intermediate level

Some related experience required

5 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

108 reviews

Intermediate level

Some related experience required

5 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the Ethics in the Age of AI Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

In this course, we will explore fundamental concepts involved in security and privacy of machine learning projects. Diving into the ethics behind these decisions, we will explore how to protect users from privacy violations while creating useful predictive models. We will also ask big questions about how businesses implement algorithms and how that affects user privacy and transparency now and in the future.

Module details

In Module 1, we are going to discuss what true anonymity and privacy mean in machine learning.

What's included

5 videos2 readings3 assignments

5 videosTotal 17 minutes

Course Introduction3 minutes
How safe is your data?5 minutes
Algorithms vs privacy3 minutes
Adversarial attacks on privacy4 minutes
Weekly Review: Privacy and convenience vs big data1 minute

2 readingsTotal 32 minutes

Netflix Prize: Breaking anonymous datasets20 minutes
Clearview AI and the Dark Side of Public Data: When Photos Become Surveillance12 minutes

3 assignmentsTotal 50 minutes

Exam: Privacy and convenience vs big data30 minutes
Anonymous datasets10 minutes
Privacy and adversarial models10 minutes

In Module 2, we are going to take a deeper look at dataset security. We will also look into methods to add privacy to existing and new datasets to protect those individuals in them

What's included

4 videos2 readings3 assignments

4 videosTotal 14 minutes

How secure is your model? 5 minutes
Noise vs signal: protecting privacy4 minutes
Implementing differential privacy4 minutes
Weekly Review1 minute

2 readingsTotal 40 minutes

Securing AI & ML: Navigating Emerging Risks20 minutes
Differential Privacy at Apple20 minutes

3 assignmentsTotal 50 minutes

Weekly Quiz30 minutes
Dataset protections10 minutes
Knowledge Check10 minutes

In Module 3, we will discuss putting ethical, private models into practice. We will explore the explainable AI movement as well as tradeoffs for the teams putting together these algorithms