Predictive Modellingdata biasAlgorithmsEthics Of Artificial IntelligenceMachine Learning (ML) AlgorithmsUnderstanding of algorithmsFamiliarity with predictive modelsOverview of ethics considersationsmachine learning fairnessEthicsMachine Learningsecurity
Ethics in the Age of AI Specialization
What's the ethical impact of AI making decisions?. Learn how to impose ethical behavior on machine models.
Offered By
Ethics in the Age of AI SpecializationLearnQuestWhat you will learn
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
Skills you will gain
About this Specialization
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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.
Applied Learning Project
AI Ethics research is an emerging field, and to prove our skills, we need to demonstrate our critical thinking and analytical ability. Since it's not reasonable to jump into a full research paper with our newly founded skills, we will instead work on 3 projects that will demonstrate your ability to analyze ethical AI across a variety of topics and situations.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
We start at the beginning, so no prior experience is necessary.
Approximately 4 months to complete
Suggested pace of 2 hours/week
English
Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Spanish
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
We start at the beginning, so no prior experience is necessary.
Approximately 4 months to complete
Suggested pace of 2 hours/week
English
Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Spanish
How the Specialization Works
Take Courses
A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Hands-on Project
Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.
Earn a Certificate
When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.
There are 4 Courses in this Specialization
Artificial Intelligence Algorithms Models and Limitations
88 ratings
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, and discuss how to make them more ethically sound.
Artificial Intelligence Data Fairness and Bias
50 ratings
In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models.
Artificial Intelligence Privacy and Convenience
33 ratings
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.
Artificial Intelligence Ethics in Action
20 ratings
AI Ethics research is an emerging field, and to prove our skills, we need to demonstrate our critical thinking and analytical ability. Since it's not reasonable to jump into a full research paper with our newly founded skills, we will instead work on 3 projects that will demonstrate your ability to analyze ethical AI across a variety of topics and situations. These projects include all the skills you've learned in this AI Ethics Specialization.
Offered by
LearnQuest
LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.
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