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Ethics in the Age of AI

As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations. In this specialization, we will explore the rise of algorithms, fundamental issues of fairness and bias in machine learning, and basic concepts involved in security and privacy of machine learning projects. We'll finish with a study of 3 projects that will allow you to put your new skills into action.

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What's inside

Four courses

Artificial Intelligence Algorithms Models and Limitations

We live in an age increasingly dominated by algorithms. As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations in the real world. Whether it's making loan decisions or re-routing traffic, machine learning models need to accurately reflect our shared values. In this course, we will explore the rise of algorithms, from the most basic to the fully-autonomous.

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Artificial Intelligence Data Fairness and Bias

In this course, we will investigate fairness and bias in machine learning. As models make important decisions, it's critical to prevent unfair predictions. We will explore human bias, dataset awareness, and more to build ethical models.

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Artificial Intelligence Privacy and Convenience

In this course, we will explore the fundamental concepts involved in the security and privacy of machine learning projects. We will dive into the ethics behind these decisions and 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.

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Artificial Intelligence Ethics in Action

AI Ethics research is an emerging field. To prove our skills, we need to demonstrate our critical thinking and analytical ability. We will work on 3 projects that will demonstrate your ability to analyze ethical AI across a variety of topics and situations.

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Learning objectives

Define predictive models and analyze how companies use them
Identify how learning algorithms are used in everyday life

Examine the ability of algorithms to influence and decide human behavior in biased ways, and methods to avoid predictive bias
Identify vulnerabilities in public data sets and analyze algorithmic privacy violations

Define predictive models and analyze how companies use them
Identify how learning algorithms are used in everyday life
Examine the ability of algorithms to influence and decide human behavior in biased ways, and methods to avoid predictive bias
Identify vulnerabilities in public data sets and analyze algorithmic privacy violations

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Time

0 hours

Level

Introductory

Via

Coursera

Institution

LearnQuest

Instructors

Brent Summers

Sabrina Moore

Level

Introductory

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