What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches?
This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values. You will examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems while also learning best practices for responsible data management, understanding the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten."
This course will help you answer questions such as who owns data, how do we value privacy, how to receive informed consent and what it means to be fair.
Data scientists and anyone beginning to use or expand their use of data will benefit from this course. No particular previous knowledge needed.
Data Science Ethics
Offered By
University of Michigan
Syllabus - What you will learn from this course
Week
11 hour to complete
What are Ethics?
Module 1 of this course establishes a basic foundation in the notion of simple utilitarian ethics we use for this course. The lecture material and the quiz questions are designed to get most people to come to an agreement about right and wrong, using the utilitarian framework taught here. If you bring your own moral sense to bear, or think hard about possible counter-arguments, it is likely that you can arrive at a different conclusion. But that discussion is not what this course is about. So resist that temptation, so that we can jointly lay a common foundation for the rest of this course.
4 videos (Total 21 min), 4 readings, 1 quiz
4 videos
What are Ethics?9m
Data Science Needs Ethics3m
Case Study: Spam (not the meat)4m
4 readings
Course Syllabus10m
Welcome Announcement10m
Help us learn more about you!10m
What are Ethics? - Introduction10m
1 practice exercise
Module 1 Quiz20m
1 hour to complete
History, Concept of Informed Consent
Early experiments on human subjects were by scientists intent on advancing medicine, to the benefit of all humanity, disregard for welfare of individual human subjects. Often these were performed by white scientists, on black subject. In this module we will talk about the laws that govern the Principle of Informed Consent. We will also discuss why informed consent doesn’t work well for retrospective studies, or for the customers of electronic businesses.
4 videos (Total 33 min), 1 quiz
4 videos
Human Subjects Research and Informed Consent: Part 28m
Limitations of Informed Consent9m
Case Study: It's Not OKCupid6m
1 practice exercise
Module 2 Quiz20m
1 hour to complete
Data Ownership
Who owns data about you? We'll explore that question in this module. A few examples of personal data include copyrights for biographies; ownership of photos posted online, Yelp, Trip Advisor, public data capture, and data sale. We'll also explore the limits on recording and use of data.
5 videos (Total 28 min), 1 quiz
5 videos
Limits on Recording and Use7m
Data Ownership Finale3m
Case Study: Rate My Professor3m
Case Study: Privacy After Bankruptcy2m
1 practice exercise
Module 3 Quiz20m
Week
22 hours to complete
Privacy
Privacy is a basic human need. Privacy means the ability to control information about yourself, not necessarily the ability to hide things. We have seen the rise different value systems with regards to privacy. Kids today are more likely to share personal information on social media, for example. So while values are changing, this doesn’t remove the fundamental need to be able to control personal information. In this module we'll examine the relationship between the services we are provided and the data we provide in exchange: for example, the location for a cell phone. We'll also compare and contrast "data" against "metadata".
7 videos (Total 53 min), 2 readings, 1 quiz
7 videos
Privacy3m
History of Privacy15m
Degrees of Privacy10m
Modern Privacy Risks12m
Case Study: Targeted Ads3m
Case Study: The Naked Mile2m
Case Study: Sneaky Mobile Apps5m
2 readings
Privacy - Introduction10m
Module 4 Discussion Prompt References10m
1 practice exercise
Module 4 Quiz20m
1 hour to complete
Anonymity
Certain transactions can be performed anonymously. But many cannot, including where there is physical delivery of product. Two examples related to anonymous transactions we'll look at are "block chains" and "bitcoin". We'll also look at some of the drawbacks that come with anonymity.
4 videos (Total 26 min), 1 quiz
4 videos
De-identification Has Limited Value: Part 17m
De-identification Has Limited Value: Part 210m
Case Study: Credit Card Statements2m
1 practice exercise
Module 5 Quiz20m
Week
32 hours to complete
Data Validity
Data validity is not a new concern. All too often, we see the inappropriate use of Data Science methods leading to erroneous conclusions. This module points out common errors, in language suited for a student with limited exposure to statistics. We'll focus on the notion of representative sample: opinionated customers, for example, are not necessarily representative of all customers.
10 videos (Total 60 min), 1 reading, 1 quiz
10 videos
Validity9m
Choice of Attributes and Measures6m
Errors in Data Processing8m
Errors in Model Design8m
Managing Change5m
Case Study: Three Blind Mice4m
Case Study: Algorithms and Race3m
Case Study: Algorithms in the Office3m
Case Study: GermanWings Crash5m
Case Study: Google Flu5m
1 reading
Data Validity - Introduction10m
1 practice exercise
Module 6 Quiz20m
1 hour to complete
Algorithmic Fairness
What could be fairer than a data-driven analysis? Surely the dumb computer cannot harbor prejudice or stereotypes. While indeed the analysis technique may be completely neutral, given the assumptions, the model, the training data, and so forth, all of these boundary conditions are set by humans, who may reflect their biases in the analysis result, possibly without even intending to do so. Only recently have people begun to think about how algorithmic decisions can be unfair. Consider this article, published in the New York Times. This module discusses this cutting edge issue.
6 videos (Total 50 min), 1 reading, 1 quiz
6 videos
Correct But Misleading Results12m
P Hacking10m
Case Study: High Throughput Biology3m
Case Study: Geopricing2m
Case Study: Your Safety Is My Lost Income10m
1 reading
Algorithmic Fairness - Introduction10m
1 practice exercise
Module 7 Quiz20m
Week
41 hour to complete
Societal Consequences
In Module 8, we consider societal consequences of Data Science that we should be concerned about even if there are no issues with fairness, validity, anonymity, privacy, ownership or human subjects research. These “systemic” concerns are often the hardest to address, yet just as important as other issues discussed before. For example, we consider ossification, or the tendency of algorithmic methods to learn and codify the current state of the world and thereby make it harder to change. Information asymmetry has long been exploited for the advantage of some, to the disadvantage of others. Information technology makes spread of information easier, and hence generally decreases asymmetry. However, Big Data sets and sophisticated analyses increase asymmetry in favor of those with ability to acquire/access.
5 videos (Total 46 min), 1 reading, 1 quiz
5 videos
Ossification7m
Surveillance4m
Case Study: Social Credit Scores7m
Case Study: Predictive Policing8m
1 reading
Societal Consequences - Introduction10m
1 practice exercise
Module 8 Quiz20m
3 hours to complete
Code of Ethics
Finally, in Module 9, we tie all the issues we have considered together into a simple, two-point code of ethics for the practitioner.
3 videos (Total 16 min), 1 reading, 2 quizzes
1 reading
Post-Course Survey10m
1 practice exercise
Module 9 Quiz10m
1 hour to complete
Attributions
This module contains lists of attributions for the external audio-visual resources used throughout the course.
4 readings
4 readings
Week 1 Attributions10m
Week 2 Attributions10m
Week 3 Attributions10m
Week 4 Attributions10m
4.6
33 ReviewsTop Reviews
Absolutely delightful to have Professor Jagadish walking us through the course. The course was informative and very stimulating. Opens up to a new world of data science ethics. Thank you!
This course is short, slow, and easy, but I ranked it five stars because the content is important in today's growing reliance on data science.
Instructor
H.V. Jagadish
Bernard A Galler Collegiate ProfessorElectrical Engineering and Computer Science
About University of Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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