Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Elec. Eng. 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?
- Exploring Ethics
- Data Science Ethics
Module 2: History, Concept of Informed Consent
- Human Subjects Research and Informed Consent
- Limitations of Informed Consent
Module 3: Data Ownership
- Introduction to Data Ownership
- Limits on Recording and Use
Module 4: Privacy
- Introduction to Privacy
- History of Privacy
- Degrees of Privacy
- Modern Privacy Risks
Module 5: Anonymity
- Introduction to Anonymity
- De-identification Has Limited Value
Module 6: Data Validity
- Introduction to Validity
- Choice of Attributes and Measures
- Errors in Data Processing
- Errors in Model Design
- Managing Change
Module 7: Algorithmic Fairness
- Introduction to Algorithmic Fairness
- Correct But Misleading Results
- P Hacking
Module 8: Societal Consequences
- Introduction to Societal Impact
- Ossification
- Surveillance
Module 9: Code of Ethics
- The Need for a Code of Ethics
Grading Policy
To pass this course and earn the certificate, learners must achieve an overall course grade of 80% or higher. Module review quizzes are worth 70% of the final grade, and a case study is worth 30%.
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
