Artificial intelligence, deep neural networks, and machine learning systems all make use of data, lots of data. But what about the data or information that is not present? The absent?

People make everyday decisions while lacking information about the options and their outcomes. To do that, we engage abductive inference (taking the best guess). This type of thinking is often referred to as common sense and it works for humans. But what about machines? Can we make algorithms use human-level common sense? Should we?

At this seminar, we will be discussing what we can learn from human intelligence, rationality and types of decision-making, and how this knowledge can be used in developing algorithmic decision-making. Specifically, we will be focusing on the productive force of absence in decision-making processes.