Explore complex, thought-provoking ethical issues in the age of AI, and the importance of responsible data stewardship

What you'll learn

Review the fundamental concepts of ethics, and discuss how data ethics differs from legal frameworks regulating data

Understand how to be an effective steward of data, and review the ethical implications of collecting and managing sensitive data

Learn how to detect and mitigate common forms of bias, including sampling, selection, algorithmic and confirmation bias

Dive into the world of AI and explore unique ethical challenges in terms of data collection, bias, and societal harm

Requirements

This is an entry-level course (no prerequisites)

Some experience working with data is helpful, but not required

Description

Data and AI Ethics are topics that most data leaders and professionals don't learn in school, or on the job.But as the volume of data grows and the impact of ML & AI algorithms continues to increase, understanding the ethical implications of our work – and how to prevent & mitigate ethical lapses – is more important than ever.We'll start this course by defining AI and Data ethics before moving on to what it means to be an ethical steward of data.From there, we'll dive into the types of bias that can be present in your data and how it can propagate into analyses and algorithms in a way that can not only raise ethical questions, but also negatively impact your company's bottom line.Next, we'll dive into the world of modern AI models, and the unique risks and ethical concerns posed by powerful generative AI tools. We'll use case studies to highlight real-life controversies, discuss how to mitigate the risk of ethical lapses, and use thought exercises to help you develop the skills to anticipate, identify, and mitigate the risk of ethical lapses in your day-to-day work.COURSE OUTLINE:Data Ethics 101Introduce the fundamental concepts of ethics, and discuss how data ethics differs from legal frameworks regulating dataEthical Data StewardshipUnderstand how to be an effective steward of data, and review the ethical implications of collecting and managing sensitive dataData & Algorithmic BiasLearn how to detect and mitigate common forms of bias, including sampling, selection, algorithmic and confirmation biasAI Ethics & ImpactDive into the world of AI and explore unique ethical challenges in terms of data collection, bias, and societal harm__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:1.5 hours of high-quality video6 real-world case studies9 thought exercises4 course quizzesData & AI Ethics ebook (50+ pages)Expert support and Q&A forum30-day Udemy satisfaction guaranteeIf you're looking for a unique and highly engaging way to learn about data and AI ethics, this course is for you.Happy learning!-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)__________Looking for our full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!See why our courses are among the TOP-RATED on Udemy:"Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C."This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M."Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.

Overview

Section 1: Getting Started

Lecture 1 Course Introduction

Lecture 2 Course Structure & Outline

Lecture 3 READ ME: Important Notes for New Students

Lecture 4 DOWNLOAD: Course Resources

Lecture 5 Setting Expectations

Section 2: Data Ethics 101

Lecture 6 What is Ethics?

Lecture 7 CASE STUDY: Cambridge Analytica

Lecture 8 Ethics vs. Law

Lecture 9 THOUGHT EXERCISE: The Trolley Problem

Lecture 10 Key Takeaways

Section 3: Ethical Data Stewardship

Lecture 11 Data Stewardship

Lecture 12 CASE STUDY: OkCupid Dataset

Lecture 13 Consent

Lecture 14 Security

Lecture 15 Privacy & Confidentiality

Lecture 16 THOUGHT EXERCISE: Genetic Databases

Lecture 17 THOUGHT EXERCISE: Social Media Database

Lecture 18 Data Ethics Regulations

Lecture 19 Key Takeaways

Section 4: Data & Algorithmic Bias

Lecture 20 Types of Bias

Lecture 21 CASE STUDY: The Eigenface Dataset

Lecture 22 The Model Training Process

Lecture 23 Effects of Data Bias

Lecture 24 THOUGHT EXERCISE: Customer Survey

Lecture 25 Algorithmic Bias

Lecture 26 CASE STUDY: Amazon Hiring Algorithm

Lecture 27 Proxy Variables

Lecture 28 THOUGHT EXERCISE: Proxy Variables

Lecture 29 Algorithmic Harm Potential

Lecture 30 Model Transparency

Lecture 31 Combatting Bias

Lecture 32 THOUGHT EXERCISE: Disaster Response Algorithm

Lecture 33 CASE STUDY: Recidivism Algorithm

Lecture 34 Algorithmic Moral Judgements

Lecture 35 THOUGHT EXERCISE: Leaving it to Humans

Lecture 36 Key Takeaways

Section 5: AI Ethics

Lecture 37 AI Ethics

Lecture 38 CASE STUDY: Artist Lawsuits

Lecture 39 Generative AI

Lecture 40 THOUGHT EXERCISE: Creator YouTube Video

Lecture 41 CASE STUDY: AI Product Reviews

Lecture 42 Hallucinations & Fraud

Lecture 43 THOUGHT EXERCISE: Celebrity Likeness

Lecture 44 Mitigating Risks of AI

Lecture 45 Key Takeaways

Section 6: Wrapping Up

Lecture 46 BONUS LESSON

Data leaders looking to build a deeper understanding of the ethical concerns prevalent in modern data products and projects,Individual contributors who want to learn more about how they can identify and mitigate potential ethical issues in their day-to-day work,Anyone looking for a thought-provoking and highly engaging course on ethics in the age of AI