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    • Data Science Ethics

    U-M Free Access Enrollment Options
    Rating

    About Data Science Ethics

    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.

    Format
    Self-Paced
    Subject
    • Data Science
    Platform
    Coursera

    Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.

    Enrollment Options

    Individuals

    This experience is available to individual learners on the following platforms:

    Enroll on Coursera

    U-M Community

    Free access is only available to current U-M students, alumni, faculty, and staff.

    U-M Free on Coursera

    Organizations

    Special pricing and tailored programming bundles available for organizational partners.

    Learn More
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    Reviews and Ratings

    4.7

    1,013 Ratings from Coursera

    Most Recent Reviews

    Read all reviews
    Excellent Course
    Good course to learn
    NA
    Good
    completely trash
    Too confusing to close the courses, instructions are poor
    This Data Science Ethics approach of the US culture is very good for me. I learned new concepts for my future career.
    theory part are boring tools and acts should be explained.
    Good course. Challenging and thought provoking. The professor was very good, but, even if you complete all assignments, your certification and grade depends upon having others in the course go in and review your assignment. Even if you review more than you are required to review, if others don't review yours, you don't pass or get your certification. So, be aware of that if you are attending a class in the hopes of obtaining a certification. It is not guaranteed even if you do all of the work.
    I've been disappointed with the course overall. it's a very interesting topic, one that's relevant to the times we live in. But I feel that many of the big questions are neither asked nor answered in the course. The presenter's background is in engineering, not philosophy. This isn't a fundamental problem in itself, but it does mean that concepts are left very vaguely defined. So 'ethics' is, at one point, defined more or less as 'what's socially agreed-up'. But in the questions as well as the videos, the terminology shifts between 'ethically right/wrong', 'appropriate', 'have to do something,' 'by rights', 'legally', and lots more besides. So it's far from clear what the right or wrong position might be in many of the thought-experiments described in the quiz questions. What's more, if an action isn't in itself ethically right, that doesn't entail that it must be ethically wrong: it could be neither. There's virtually no input from other voices, too: no discussions with, say, experts in the field of data ethics, or moral philosophers, or whatever. At one point in week 1, the presenter correctly points out that ''data ownership is really complex''. So it'd be useful to have a MOOC that makes it less complex: that asks hard questions, and critically examines the range of possible answers. Unfortunately, this MOOC isn't it.

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