Über diesen Kurs
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Englisch

Untertitel: Englisch

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Anfänger“

Ca. 16 Stunden zum Abschließen

Empfohlen: 4 weeks, 3-4 hours/week...

Englisch

Untertitel: Englisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
1 Stunde zum Abschließen

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 (Gesamt 21 min), 4 Lektüren, 1 Quiz
4 Videos
What are Ethics?9m
Data Science Needs Ethics3m
Case Study: Spam (not the meat)4m
4 Lektüren
Course Syllabus10m
Welcome Announcement10m
Help us learn more about you!10m
What are Ethics? - Introduction10m
1 praktische Übung
Module 1 Quiz20m
1 Stunde zum Abschließen

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 (Gesamt 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 praktische Übung
Module 2 Quiz20m
1 Stunde zum Abschließen

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 (Gesamt 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 praktische Übung
Module 3 Quiz20m
Woche
2
2 Stunden zum Abschließen

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 (Gesamt 53 min), 2 Lektüren, 1 Quiz
7 Videos
History of Privacy15m
Degrees of Privacy10m
Modern Privacy Risks12m
Case Study: Targeted Ads3m
Case Study: The Naked Mile2m
Case Study: Sneaky Mobile Apps5m
2 Lektüren
Privacy - Introduction10m
Module 4 Discussion Prompt References10m
1 praktische Übung
Module 4 Quiz20m
1 Stunde zum Abschließen

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 (Gesamt 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 praktische Übung
Module 5 Quiz20m
Woche
3
2 Stunden zum Abschließen

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 (Gesamt 60 min), 1 Lektüre, 1 Quiz
10 Videos
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 Lektüre
Data Validity - Introduction10m
1 praktische Übung
Module 6 Quiz20m
1 Stunde zum Abschließen

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 (Gesamt 50 min), 1 Lektüre, 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 Lektüre
Algorithmic Fairness - Introduction10m
1 praktische Übung
Module 7 Quiz20m
Woche
4
1 Stunde zum Abschließen

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 (Gesamt 46 min), 1 Lektüre, 1 Quiz
5 Videos
Ossification7m
Surveillance4m
Case Study: Social Credit Scores7m
Case Study: Predictive Policing8m
1 Lektüre
Societal Consequences - Introduction10m
1 praktische Übung
Module 8 Quiz20m
3 Stunden zum Abschließen

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 (Gesamt 16 min), 1 Lektüre, 2 Quiz
3 Videos
Wrap Up2m
Case Study: Algorithms and Facial Recognition4m
1 Lektüre
Post-Course Survey10m
1 praktische Übung
Module 9 Quiz10m
1 Stunde zum Abschließen

Attributions

This module contains lists of attributions for the external audio-visual resources used throughout the course....
4 Lektüren
4 Lektüren
Week 1 Attributions10m
Week 2 Attributions10m
Week 3 Attributions10m
Week 4 Attributions10m
4.6
33 BewertungenChevron Right

Top-Bewertungen

von AYMar 18th 2019

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!

von JMJul 1st 2018

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.

Dozent

Avatar

H.V. Jagadish

Bernard A Galler Collegiate Professor
Electrical Engineering and Computer Science

Über 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....

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