Data Science Ethics

Identify the best practices for ethical and responsible data management This 4-week course will develop your knowledge of data ethics as you explore the questions around the ethics of big data and AI. Explore the ethics of data collection and data sharing From developments in healthcare to keeping cities safe, we’ve found a huge variety of useful ways of using data collection and sharing. But how does collecting data affect our personal privacy? And, how much should we trust algorithmic fairness? Led by experts at the University of Michigan, you’ll use the framework of ethics to analyse the societal consequences of data science and identify why data privacy is important. Consider how big data is affecting the modern world As society grapples with defining shared values regarding what is okay and what isn’t when it comes to data, you’ll examine how data impacts on our principles of fairness, accountability, and transparency, and discover why it’s important we develop a shared set of societal values when it comes to data ethics. While laws will be mentioned in places, this course will invite you to take a compliance viewpoint, where you think not in terms of what one can do (legally), but instead in terms of what one should do. Learn with the director of the Michigan Institute for Data Science The leading educator on this course is Professor HV Jagadish, Professor of Electrical Engineering and Computer Science at the University of Michigan and distinguished scientist at the Michigan Institute for Data Science. Professor Jagadish is well known for his broad-ranging research on information management and has published over 200 major papers and 37 patents. Whether you’re studying data science or you’re a practising data scientist, you’ll enhance your knowledge of data ethics and privacy with a leading researcher in data science and management. This course is designed for data scientists and enthusiasts interested in developing their understanding of how data science impacts our society, for better or worse.