Bernard A Galler Collegiate Professor, Electrical Engineering and Computer Science
Data Science Ethics
What You'll Learn
- Examine the ethical and privacy implications of collecting and managing big data.
- Explore the broader impact of the data science field on modern society.
- Understand who owns data, how we value privacy, how to receive informed consent and what it means to be fair.
10 Modules
20 Hours
2 hrs per module (approx.)
About Data Science Ethics
As patients, we care about the privacy of our medical record; but as patients, we also wish to benefit from the analysis of data in medical records. As citizens, we want a fair trial before being punished for a crime; but as citizens, we want to stop terrorists before they attack us. As decision-makers, we value the advice we get from data-driven algorithms; but as decision-makers, we also worry about unintended bias. Many data scientists learn the tools of the trade and get down to work right away, without appreciating the possible consequences of their work.
This course focused on ethics specifically related to data science will provide you with the framework to analyze these concerns. This framework is based on ethics, which are shared values that help differentiate right from wrong. Ethics are not law, but they are usually the basis for laws.
Everyone, including data scientists, will benefit from this course. No previous knowledge is needed.
Skills You'll Gain
- Bayesian Statistics
- Data Analysis
- Data Ethics
- Policy Analysis
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Data Science Ethics establishes a shared ethical foundation using a utilitarian framework to evaluate right and wrong in data-driven decision making. Learners examine informed consent, data ownership, privacy, algorithmic fairness, and societal consequences of data science, culminating in the creation and evaluation of professional codes of ethics.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: What Are Ethics?
- Reading: Course Syllabus
- Reading: Welcome Announcement
- Reading: Help us learn more about you!
- Reading: What are Ethics? - Introduction
- Video: Data Science Ethics - Course Preview
- Video: What are Ethics?
- Video: Data Science Needs Ethics
- Video: Case Study: Spam (not the meat)
- Discussion Prompt: Module 1 Discussion
Module 2: History, Concept of Informed Consent
- Video: Human Subjects Research and Informed Consent: Part 1
- Video: Human Subjects Research and Informed Consent: Part 2
- Video: Limitations of Informed Consent
- Video: Case Study: It's Not OKCupid
- Discussion Prompt: Module 2 Discussion
Module 3: Data Ownership
- Video: Data Ownership
- Video: Limits on Recording and Use
- Video: Data Ownership Finale
- Video: Case Study: Rate My Professor
- Video: Case Study: Privacy After Bankruptcy
- Discussion Prompt: Module 3 Discussion
Module 4: Privacy
- Reading: Privacy - Introduction
- Video: Privacy
- Video: History of Privacy
- Video: Degrees of Privacy
- Video: Modern Privacy Risks
- Video: Case Study: Targeted Ads
- Video: Case Study: The Naked Mile
- Video: Case Study: Sneaky Mobile Apps
- Discussion Prompt: Module 4 Discussion
- Reading: Module 4 Discussion Prompt References
Module 5: Anonymity
- Video: Anonymity
- Video: De-identification Has Limited Value: Part 1
- Video: De-identification Has Limited Value: Part 2
- Video: Case Study: Credit Card Statements
- Discussion Prompt: Module 5 Discussion
Module 6: Data Validity
- Reading: Data Validity - Introduction
- Video: Validity
- Video: Choice of Attributes and Measures
- Video: Errors in Data Processing
- Video: Errors in Model Design
- Video: Managing Change
- Video: Case Study: Three Blind Mice
- Video: Case Study: Algorithms and Race
- Video: Case Study: Algorithms in the Office
- Video: Case Study: GermanWings Crash
- Video: Case Study: Google Flu
- Discussion Prompt: Module 6 Discussion
Module 7: Algorithmic Fairness
- Reading: Algorithmic Fairness - Introduction
- Video: Algorithmic Fairness
- Video: Correct But Misleading Results
- Video: P Hacking
- Video: Case Study: High Throughput Biology
- Video: Case Study: Geopricing
- Video: Case Study: Your Safety Is My Lost Income
- Discussion Prompt: Module 7 Discussion
Module 8: Societal Consequences
- Reading: Societal Consequences - Introduction
- Video: Societal Impact
- Video: Ossification
- Video: Surveillance
- Video: Case Study: Social Credit Scores
- Video: Case Study: Predictive Policing
- Discussion Prompt: Module 8 Discussion
Module 9: Code of Ethics
- Video: Code of Ethics
- Video: Wrap Up
- Video: Case Study: Algorithms and Facial Recognition
- Reading: Post-Course Survey
Module 10: Attributions
- Reading: Week 1 Attributions
- Reading: Week 2 Attributions
- Reading: Week 3 Attributions
- Reading: Week 4 Attributions
- Reading: Keep Learning with Michigan Online
Grading Policy
Learners must pass all assignments and earn an overall grade of 70% to pass. The course grade is based on nine quizzes worth 70% total, and a peer-graded assignment worth 30% of the final grade.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No prior experience required
