by
Prof.Norman Sieroka(Universität Bremen), DrTammo Lossau(Universität Bremen)
→
Europe/Berlin
Zoom
Zoom
Description
Motivation
A critical awareness (“Critical Thinking”, see below) is crucial for an appropriate and reasonable assessment of data preparation, sharing, and utilization in the context of research data management, data protection, and data science applications.
Critical thinking facilitates the establishment of common languages across disciplines while being aware of limits and difficulties. Thereby, it is essential for cooperative and future-oriented research.
Learning Contents
This session introduces the concept of critical thinking and provides a rough overview over broad societal concerns and philosophical debates around data science and artificial intelligence. For instance, it seems inescapable that we lose some of our own autonomy once our cars start driving autonomously and our houses become smarter and smarter. Computers outsmart us in number crunching since decades, but will they also outsmart us in creativity? Will they become the “better scientists” or will there always remain a difference between “pure prediction” and “real understanding”? Is predictive success acceptable even if it comes with a loss in transparency? After all, transparency is something we are very much worried about not only in science but in all kinds of political and societal contexts. At the same time, privacy and data protection laws are a major theme in public discourse as well. Consider tracking apps, for instance—do we really want to become transparent citizens and consumers, X-rayed as it were by a machine learning algorithm no one might actually understand?
Learning Outcomes
Critical Thinking: Critical reflection on one’s own research/work and developing appreciation of other disciplines, their mindsets and ways of thinking.