Privacy. Security. Risk. 2016
Moderator: Sol Bermann, CIPP/US, Privacy Officer, IT Policy and Compliance Strategist, University of Michigan; Mike Daniel, Director, Policy and Operations, Office of Academic Innovation, University of Michigan; Kent Wada, CPO and Director, Strategic IT Policy, UCLA
Colleges and universities are increasingly leveraging big data and data science to improve student learning outcomes and retention and graduation rates. Predictive analytics promises to widen the pathways for student success by mining through troves of data and personalizing the education experience. However, this also raises critical privacy and ethical concerns: Do traditional privacy practices and principles hold up in world of big data and analytics? How do you manage legal and regulatory compliance in a big data world? How do we counteract potential biases or profiling concerns that creep into data-driven algorithms? When algorithms are used to drive student success, how do we define “success”? Does expansive data collection conflict with academic freedom? This session will explore approaches to enabling the analytics revolution while balancing it with privacy and ethical considerations.
What you’ll take away:
- Understand the benefits of big data and analytics in the education environment
- Identify privacy and ethical concerns associated with student analytics
- Explore new privacy modalities, frameworks and risk-benefit models that address the unique challenges of big data and open data