The increasing number of networked information technologies—including the Internet of Things (IoT), wearables, ubiquitous sensing, social sharing platforms, and other AI-driven systems—is generating a tremendous amount of data about individuals, companies, and societies.

These technologies offer enormous benefits but also create enormous risks to individual privacy and national security. Further, the ease with which data can be collected from online sources, analyzed, and inferences drawn about individual users raises a wide range of ethical questions about these technologies, their creators, and their users.

In this course, students will evaluate major privacy and security questions raised by big data and related technologies. Students will  learn about the history of research ethics and consider how ethical frameworks can and should be applied to digital data. They will work through case studies from real-world scenarios to understand the complex interactions between data security, privacy, and ethics in modern businesses.

Learning Objectives

After successfully completing this course, you will be able to:

1.    Identify and explain basic ethical and policy-based frameworks for working with big data and apply these frameworks to real- world cases.

2.    Explain differences between and shared values across data, ethics, and society.

3.    Identify situations where data is sensitive, assess the risks, and describe how various stakeholders could respond to those risks.

4.    Describe how to minimize privacy/security compromises through the data lifecycle (from collection through dissemination).

5.    Implement good security and privacy practices in personal data storage, use, and reporting.

Course Structure

This class will be conducted in person. Weekly content will become available on Fridays at 11:59 PM ET. This includes readings, videos, and assignments. All assignments for that week are due the following Friday, at 11:59 PM ET.

During any office hour Zoom sessions, please turn your video on.

General announcements will be sent via ELMS Announcements, and it is your responsibility to read them regularly.

Do NOT use ELMS email. See above for specifics.

Activities, Learning Assessments, and Expectations from Students

Case Studies (50%): In this class, we’ll be using case studies from real-world examples that highlight concepts from the class at the intersection of big data, ethics, and privacy/security. For each case study, you will be assigned a reading that overviews the case study, and then you will complete a 600-800-word write-up on the case study based on the guiding questions I provide. I expect to provide seven case study assignments throughout the semester.

Other Assignments (10%): There will be discussions or other assignments related to study topics. One or more Study Cases might be replaced by different assignments as needed.

In-Class Participation/Group Activities (25% total): Participation and engagement in in-class activities are very important for successful learning. We will do in-class discussions and exercises. In-class exercises may be individual or performed in groups.

Final Exam (15%): The goal of the final exam is to assess whether you have successfully met the learning outcomes of this class. The exam will require you to complete 3-4 essays on topics related to the content covered in the course. The exam questions will be assigned on the final day of classes, and you will have until the scheduled final exam date and time to submit your responses electronically. There will be no physical exam during the scheduled exam time.