As patients, we care about the privacy of our medical record; but as patients, we also wish to benefit from the analysis of data in medical records. As citizens, we want a fair trial before being punished for a crime; but as citizens, we want to stop terrorists before they attack us. As decision-makers, we value the advice we get from data-driven algorithms; but as decision-makers, we also worry about unintended bias. Many data scientists learn the tools of the trade and get down to work right away, without appreciating the possible consequences of their work.
This course focused on ethics specifically related to data science will provide you with the framework to analyze these concerns. This framework is based on ethics, which are shared values that help differentiate right from wrong. Ethics are not law, but they are usually the basis for laws.
Everyone, including data scientists, will benefit from this course. No previous knowledge is needed.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
DS101x Data Science Ethics is a 4-week survey of ethical issues that arise in data science offered by the University of Michigan through the edX MOOC platform. The course includes 9 modules that begin with a basic overview of ethics and the history of ethics, followed by discussions of data ownership, privacy, anonymit
DS101x Data Science Ethics is a 4-week survey of ethical issues that arise in data science offered by the University of Michigan through the edX MOOC platform. The course includes 9 modules that begin with a basic overview of ethics and the history of ethics, followed by discussions of data ownership, privacy, anonymity, data validity, algorithmic fairness and society consequences. There are no real prerequisites to take the course, although some familiarity with data science will give you more insight into the material. Grading is based on 9 quizzes (one for each module) that allow unlimited attempts and a written, peer-graded case study. You can earn enough points to pass the course without doing the peer-graded assignment.
Each module is divided into three parts: video lectures, case study lectures and a quiz. The video lecture sections generally consist of about three video segments that run from 4 to 12 minutes each that introduce and discuss major course topics. The lecturer speaks clearly and the slides and video quality are good, but the lecturer's delivery is somewhat monotonous. I recommend increasing the playback speed to keep things moving along at a reasonable pace. The case study lectures look at specific real-life instances of ethical issues presented in the main video lectures. The quizzes are mostly true/false response word problems and you can submit your answers as many times as you want so it is easy to get 100 percent.
Data Science Ethics raises a variety of ethical issues data science practitioners should consider when collecting and using data, but ethics are largely subjective so it can't provide definitive prescriptions about what you should or shouldn't do. It will help you raise relevant ethical questions, but it won't necessarily help you answer them. I found that the case studies were often more relevant and interesting than the main lectures.
Data Science Ethics provides a nice overview of some of the ethical implications of data science and requires a minimal time commitment. Just be aware that the subject matter is subjective so the professor can only really present his take on the issues.
I give Data Science Ethics 3.5 out of 5 stars: Good.
This course presents some interesting case studies and raises many data sciences ethical problems worth thinking about. However, the course lacks any academic rigour and the introduction to ethics section is far too brief. This leads to the lack of a solid framework from which the ethical issues presented can be analy
This course presents some interesting case studies and raises many data sciences ethical problems worth thinking about. However, the course lacks any academic rigour and the introduction to ethics section is far too brief. This leads to the lack of a solid framework from which the ethical issues presented can be analysed. Ideally, each issue should be consider from a variety of ethical frameworks. The course is essentially the lecturers subjective opinion on ethical matters and would make more sense as a presentation by the lecturer rather than billed as a course. This is an important subject and I hope that future courses will have more solid foundations, and less subjective discussions.