Об этом курсе
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Субтитры: Английский

Программа курса: что вы изучите

Неделя
1
1 ч. на завершение

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.

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4 видео ((всего 21 мин.)), 4 материалов для самостоятельного изучения, 1 тест
4 видео
What are Ethics?9мин
Data Science Needs Ethics3мин
Case Study: Spam (not the meat)4мин
4 материала для самостоятельного изучения
Course Syllabus10мин
Welcome Announcement10мин
Help us learn more about you!10мин
What are Ethics? - Introduction10мин
1 практического упражнения
Module 1 Quiz20мин
1 ч. на завершение

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.

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4 видео ((всего 33 мин.)), 1 тест
4 видео
Human Subjects Research and Informed Consent: Part 28мин
Limitations of Informed Consent9мин
Case Study: It's Not OKCupid6мин
1 практического упражнения
Module 2 Quiz20мин
1 ч. на завершение

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.

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5 видео ((всего 28 мин.)), 1 тест
5 видео
Limits on Recording and Use7мин
Data Ownership Finale3мин
Case Study: Rate My Professor3мин
Case Study: Privacy After Bankruptcy2мин
1 практического упражнения
Module 3 Quiz20мин
Неделя
2
2 ч. на завершение

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".

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7 видео ((всего 53 мин.)), 2 материалов для самостоятельного изучения, 1 тест
7 видео
Privacy3мин
History of Privacy15мин
Degrees of Privacy10мин
Modern Privacy Risks12мин
Case Study: Targeted Ads3мин
Case Study: The Naked Mile2мин
Case Study: Sneaky Mobile Apps5мин
2 материала для самостоятельного изучения
Privacy - Introduction10мин
Module 4 Discussion Prompt References10мин
1 практического упражнения
Module 4 Quiz20мин
1 ч. на завершение

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.

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4 видео ((всего 26 мин.)), 1 тест
4 видео
Anonymity5мин
De-identification Has Limited Value: Part 17мин
De-identification Has Limited Value: Part 210мин
Case Study: Credit Card Statements2мин
1 практического упражнения
Module 5 Quiz20мин
Неделя
3
2 ч. на завершение

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.

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10 видео ((всего 60 мин.)), 1 материал для самостоятельного изучения, 1 тест
10 видео
Validity9мин
Choice of Attributes and Measures6мин
Errors in Data Processing8мин
Errors in Model Design8мин
Managing Change5мин
Case Study: Three Blind Mice4мин
Case Study: Algorithms and Race3мин
Case Study: Algorithms in the Office3мин
Case Study: GermanWings Crash5мин
Case Study: Google Flu5мин
1 материала для самостоятельного изучения
Data Validity - Introduction10мин
1 практического упражнения
Module 6 Quiz20мин
1 ч. на завершение

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.

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6 видео ((всего 50 мин.)), 1 материал для самостоятельного изучения, 1 тест
6 видео
Correct But Misleading Results12мин
P Hacking10мин
Case Study: High Throughput Biology3мин
Case Study: Geopricing2мин
Case Study: Your Safety Is My Lost Income10мин
1 материала для самостоятельного изучения
Algorithmic Fairness - Introduction10мин
1 практического упражнения
Module 7 Quiz20мин
Неделя
4
1 ч. на завершение

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.

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5 видео ((всего 46 мин.)), 1 материал для самостоятельного изучения, 1 тест
5 видео
Ossification7мин
Surveillance4мин
Case Study: Social Credit Scores7мин
Case Study: Predictive Policing8мин
1 материала для самостоятельного изучения
Societal Consequences - Introduction10мин
1 практического упражнения
Module 8 Quiz20мин
3 ч. на завершение

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.

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3 видео ((всего 16 мин.)), 1 материал для самостоятельного изучения, 2 тестов
3 видео
Wrap Up2мин
Case Study: Algorithms and Facial Recognition4мин
1 материала для самостоятельного изучения
Post-Course Survey10мин
1 практического упражнения
Module 9 Quiz10мин
1 ч. на завершение

Attributions

This module contains lists of attributions for the external audio-visual resources used throughout the course.

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4 материалов для самостоятельного изучения
4 материала для самостоятельного изучения
Week 1 Attributions10мин
Week 2 Attributions10мин
Week 3 Attributions10мин
Week 4 Attributions10мин
4.6
Рецензии: 33Chevron Right

Лучшие отзывы о курсе Data Science Ethics

автор: 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!

автор: 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.

Преподаватели

Avatar

H.V. Jagadish

Bernard A Galler Collegiate Professor
Electrical Engineering and Computer Science

О Мичиганский университет

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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