Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
The only reason to not give 5 stars is the need for an audit option where i can learn the concepts without needing to turn in assignments - i'm not looking for a grade. But the course is awesome!
Very good course. I learned a lot from this course.
I learned a lot about ethical issues and computer Science. Good lectures, good reading material, but a whole lot of writing
A course full of valuable information and beautiful skills Thank you so much I hope to be with you in other courses
This course was a surprise and covered more material than I expected. I really enjoyed it. My only critique would be to add more current articles and cases.
Leaned a lot of the ethical frameworks when being in the real professional world.
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Great course about ethics in data science and general. But please calculate a lot of more time for reading and writing as stated.
Good course. It was tough to write the report, but I was able to gain knowledge about ethical issues in data science, such as algorithmic bias and unemployment due to AI.
I learned a lot about ethical issues and computer Science. Good lectures, good reading material, but a whole lot of writing
This is an awesome general overview on the ethical issues we are likely to run into as data scientits and researchers.
A course full of valuable information and beautiful skills Thank you so much I hope to be with you in other courses
The only reason to not give 5 stars is the need for an audit option where i can learn the concepts without needing to turn in assignments - i'm not looking for a grade. But the course is awesome!
I loved this course and admired the drive for objectivity and consideration that Prof Schnabel put into his presentations, the global spirit. I feel like this field is only just beginning and ethics in general is a topic that should be part of the risk assessment for complex (artificial intelligence) projects, with a view similar to gaming out the consequences of events and asking whether they would break our own universalisable rules ( in military strategy). Call it strategic ethics. Such a research proposition would take into account the fuzziness of outcomes, intention-sets, and the relativity of valuations over intention-action-outcome triples. Culture and cultural diversity even across the American landscape were super interesting. Noticing how in tech there may on average be a bias against women. And really bringing to home the analysis of one's own intentions is something that I will return to again and again. I think that there is going to be plenty of work on how we interact with these systems ethically, and how we design and adjust/or engineer ethically and sustainably (which I believe correlates with ethical action).
Very important material. In my opinion, would be great to update some material or to add a couple of new blocks. I understand the challenge: the industry is changing with a dramatic speed. However, some information from 2020 feels like it comes from the previous life. Nonetheless, it's a great course with an extremely valuable perspective.
I thought before that this course would be easy, but it turns out I spent even more time in this course than some technical courses. The course itself was very impressive, and I appreciate the effort that CU made to equip us with ethical thinking, which is essential in data science practice.
A great introduction to ethical issues in data science. This course covers a broad range of topics, including privacy, security, algorithmic bias and as well as a number of frameworks for analyzing ethical issues.
This course was a surprise and covered more material than I expected. I really enjoyed it. My only critique would be to add more current articles and cases.
Great, massive practices of analysis in ethics. It is rare opportunity, but crucial skill in practice.
Leaned a lot of the ethical frameworks when being in the real professional world.
Very good course. I learned a lot from this course.
Learned a lot. Focused on the main topics.
Es un curso muy completo
Excellent class!
excelente
thank you