À propos de ce cours
4.6
53 notes
24 avis
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100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
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Niveau débutant

Niveau débutant

Heures pour terminer

Approx. 15 heures pour terminer

Recommandé : 4 weeks, 3-4 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau débutant

Niveau débutant

Heures pour terminer

Approx. 15 heures pour terminer

Recommandé : 4 weeks, 3-4 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
Heures pour terminer
1 heures pour terminer

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....
Reading
4 vidéos (Total 21 min), 4 lectures, 1 quiz
Video4 vidéos
What are Ethics?9 min
Data Science Needs Ethics3 min
Case Study: Spam (not the meat)4 min
Reading4 lectures
Course Syllabus10 min
Welcome Announcement10 min
Help us learn more about you!10 min
What are Ethics? - Introduction10 min
Quiz1 exercices pour s'entraîner
Module 1 Quiz20 min
Heures pour terminer
1 heures pour terminer

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....
Reading
4 vidéos (Total 33 min), 1 quiz
Video4 vidéos
Human Subjects Research and Informed Consent: Part 28 min
Limitations of Informed Consent9 min
Case Study: It's Not OKCupid6 min
Quiz1 exercices pour s'entraîner
Module 2 Quiz20 min
Heures pour terminer
1 heures pour terminer

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. ...
Reading
5 vidéos (Total 28 min), 1 quiz
Video5 vidéos
Limits on Recording and Use7 min
Data Ownership Finale3 min
Case Study: Rate My Professor3 min
Case Study: Privacy After Bankruptcy2 min
Quiz1 exercices pour s'entraîner
Module 3 Quiz20 min
Semaine
2
Heures pour terminer
2 heures pour terminer

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"....
Reading
7 vidéos (Total 53 min), 2 lectures, 1 quiz
Video7 vidéos
Privacy3 min
History of Privacy15 min
Degrees of Privacy10 min
Modern Privacy Risks12 min
Case Study: Targeted Ads3 min
Case Study: The Naked Mile2 min
Case Study: Sneaky Mobile Apps5 min
Reading2 lectures
Privacy - Introduction10 min
Module 4 Discussion Prompt References10 min
Quiz1 exercices pour s'entraîner
Module 4 Quiz20 min
Heures pour terminer
1 heures pour terminer

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....
Reading
4 vidéos (Total 26 min), 1 quiz
Video4 vidéos
De-identification Has Limited Value: Part 17 min
De-identification Has Limited Value: Part 210 min
Case Study: Credit Card Statements2 min
Quiz1 exercices pour s'entraîner
Module 5 Quiz20 min
Semaine
3
Heures pour terminer
2 heures pour terminer

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....
Reading
10 vidéos (Total 60 min), 1 lecture, 1 quiz
Video10 vidéos
Choice of Attributes and Measures6 min
Errors in Data Processing8 min
Errors in Model Design8 min
Managing Change5 min
Case Study: Three Blind Mice4 min
Case Study: Algorithms and Race3 min
Case Study: Algorithms in the Office3 min
Case Study: GermanWings Crash5 min
Case Study: Google Flu5 min
Reading1 lectures
Data Validity - Introduction10 min
Quiz1 exercices pour s'entraîner
Module 6 Quiz20 min
Heures pour terminer
1 heures pour terminer

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....
Reading
6 vidéos (Total 50 min), 1 lecture, 1 quiz
Video6 vidéos
Correct But Misleading Results12 min
P Hacking10 min
Case Study: High Throughput Biology3 min
Case Study: Geopricing2 min
Case Study: Your Safety Is My Lost Income10 min
Reading1 lectures
Algorithmic Fairness - Introduction10 min
Quiz1 exercices pour s'entraîner
Module 7 Quiz20 min
Semaine
4
Heures pour terminer
1 heures pour terminer

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. ...
Reading
5 vidéos (Total 46 min), 1 lecture, 1 quiz
Video5 vidéos
Ossification7 min
Surveillance4 min
Case Study: Social Credit Scores7 min
Case Study: Predictive Policing8 min
Reading1 lectures
Societal Consequences - Introduction10 min
Quiz1 exercices pour s'entraîner
Module 8 Quiz20 min
Heures pour terminer
3 heures pour terminer

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....
Reading
3 vidéos (Total 16 min), 1 lecture, 2 quiz
Video3 vidéos
Wrap Up2 min
Case Study: Algorithms and Facial Recognition4 min
Reading1 lectures
Post-Course Survey10 min
Quiz1 exercices pour s'entraîner
Module 9 Quiz10 min
Heures pour terminer
1 heures pour terminer

Attributions

This module contains lists of attributions for the external audio-visual resources used throughout the course....
Reading
4 lectures
Reading4 lectures
Week 1 Attributions10 min
Week 2 Attributions10 min
Week 3 Attributions10 min
Week 4 Attributions10 min
4.6
24 avisChevron Right

Meilleurs avis

par 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.

par SMMay 15th 2018

Excellent Course. Gives interesting and detailed perspectives on ethical matters related to how data can be used and should be used.

Enseignants

Avatar

H.V. Jagadish

Bernard A Galler Collegiate Professor
Electrical Engineering and Computer Science

À propos de 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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