Big Data, Artificial Intelligence, and Ethics
- 4.6
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Course Summary
Explore the ethical and societal implications of big data and AI in this course. Learn how to navigate the complex landscape of data collection, privacy, bias, and more.Key Learning Points
- Understand the ethical and societal implications of big data and AI
- Learn how to identify and mitigate bias in data collection and analysis
- Explore the role of privacy in the age of big data
- Examine the impact of big data and AI on society and democracy
Related Topics for further study
Learning Outcomes
- Understand the ethical and societal implications of big data and AI
- Identify and mitigate bias in data collection and analysis
- Examine the impact of big data and AI on society and democracy
Prerequisites or good to have knowledge before taking this course
- Basic understanding of data collection and analysis
- Familiarity with AI concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Ethics
- AI for Everyone
- Data Science Ethics
Related Education Paths
Notable People in This Field
- Higgins Professor of Natural Sciences at Harvard University
- Senior Principal Researcher at Microsoft Research New York City
Related Books
Description
This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence. With more than 99% of all mediated information in digital format and with 98% of the world population using digital technology, humanity produces an impressive digital footprint. In theory, this provides unprecedented opportunities to understand and shape society. In practice, the only way this information deluge can be processed is through using the same digital technologies that produced it. Data is the fuel, but machine learning it the motor to extract remarkable new knowledge from vasts amounts of data. Since an important part of this data is about ourselves, using algorithms in order to learn more about ourselves naturally leads to ethical questions. Therefore, we cannot finish this course without also talking about research ethics and about some of the old and new lines computational social scientists have to keep in mind. As hands-on labs, you will use IBM Watson’s artificial intelligence to extract the personality of people from their digital text traces, and you will experience the power and limitations of machine learning by teaching two teachable machines from Google yourself.
Knowledge
- Define and discuss big data opportunities and limitations.
- Work with IBM Watson and analyze a personality through Natural Language Programming (NLP).
- Examine how AI is used through case studies.
- Examine and discuss the roles ethics play in AI and big data.
Outline
- Getting Started and Big Data Opportunities
- What is this Specialization About?
- Course Introduction
- Big Data Overview
- What is "Big Data"?
- Digital Footprint
- Political Data-fusion and No-Sampling (Part 1)
- Political Data-fusion and No-Sampling (Part 2)
- Real-time
- Machine Learning
- Machine Learning Recommender Systems
- About UCCSS
- A Note From UC Davis
- Optional/Complementary
- Module 1 Quiz
- Big Data Limitations
- Big Data Limitations Overview
- Big Data Limitations
- Footprint ≠Representativeness
- Data ≠Reality
- Meaning ≠Meaningful
- Discrimination ≠Personalization
- Correlation ≠Causation
- Past ≠Future
- Welcome to Peer Review Assignments!
- Natural Language Processing (NLP) Assignment Task
- Module 2 Quiz
- Artificial Intelligence
- Introduction to Artificial Intelligence
- A Short History of AI
- State of the Art
- The Most Intelligent Gamer
- Search and Robotics
- Vision and Machine Learning
- AI Challenges
- Moral Frames
- Predictions From Morals
- Moral Brain Signatures
- Computational fMRI
- (A Personal) History of Dialogue Systems
- The Art of Dialogue
- Making Conversations
- AI Telling Stories
- Optional/Complementary
- Module 3 Quiz
- Research Ethics
- Human Downgrading
- Attention Economy
- Exploiting Cognitive Biases
- Persuasive Tech is Everywhere
- Digital Exit Strategy - Part 1
- Digital Exit Strategy - Part 2
- Digital Exit Strategy - Part 3
- Digital Exit Strategy - Part 4
- Introduction to Research Ethics
- Origins: Unethical Medical Research
- Unethical Social Research
- Taking Responsibility
- The Common Rule
- Ethical Computational Social Science
- Concerns of an AI Pioneer
- Walker on Ethics (Complementary)
- Shelton on Ethics (Complementary)
- Language Acquisition (Complementary)
- Modeling Framework (Complementary)
- Computational Model (Complementary)
- Lessons Learned (Complementary)
- Slaughterbots
- Module 4 Quiz
Summary of User Reviews
Discover the ethical implications of big data and AI in this highly-rated course on Coursera. Users have praised the comprehensive content, engaging lectures, and thought-provoking assignments.Key Aspect Users Liked About This Course
Comprehensive contentPros from User Reviews
- Engaging lectures
- In-depth exploration of ethical issues
- Practical assignments to apply concepts
- Great insights into the impact of AI and big data on society
- Interactive discussion forums for peer learning
Cons from User Reviews
- Some users found the course too technical
- Course pacing can be slow at times
- Limited opportunities for personal interaction with instructors
- Some assignments were repetitive
- Some users found the course lacking in real-world case studies
Martin Hilbert
4.6 Raiting
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