In data intelligence, humans are increasingly relying on automated machine-centric processes. The volume and speed offered by computers, coupled with the inspiration for artificial intelligence, will continue to be the driving force for the development and deployment of automated technology for data intelligence. Even if it might not be the right time to establish a scholarly discipline for studying the society and the relationships among machines, we can be absolutely certain that it is timely to study the relationships between humans and machines in the context of data intelligence.

This course examines the roles of humans and machines in data intelligence, and the collaboration, cooperation, contention, and competition between them. It brings together a structured discourse by drawing theories and evidences from mathematics, social science, computer science, and cognitive science. The course encourages students to use social science research methodologies in their comparative analysis of human- and machine-centric processes for data intelligence. The topics to be covered include:

  1. Concepts of data, information, and knowledge in different disciplines
  2. Categorization of data intelligence processes
  3. Human factors in human-centric data intelligence processes
  4. Mathematical limits of machine-centric data intelligence processes
  5. Cost-benefit analysis of data intelligence workflows
  6. Social, economic, and scientific impact of data intelligence
  7. Governance and management of data intelligence

Teaching is through a combination of lectures and classes, and is supplemented by co-learning activities in the form of student presentations. The course requires one piece of written work and a class presentation from each student as part of the assessed coursework.

Learning Outcomes

Students are expected to:

  • Appreciate the relative merits and demerits of human- and machine-centric processes in data intelligence;
  • Be conversant with a collection of machine-centric techniques for data intelligence and their mathematical limitation;
  • Be conversant with a collection of human factors in data intelligence;
  • Gain confidence and competence in performing comparative analysis;
  • Understand the necessity for involving humans in data intelligence, and be knowledgeable about means for achieving this; and
  • Engage in discussions on advanced research topics.
This page was last modified on 3 October 2018