Intro To Data & Ai Ethics
Published 10/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
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
Overview
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 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 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
