The Western society is increasingly relying on technology-supported data-driven decision-making practices for everything from policy development and customer engagement to critical infrastructure management and public administration. The digital world currently generates more than 1.7 billion bytes per minute (Madelin, 2013) and computational power is exploding, ushering in the age of big data. Many of the systems that support data-driven decision-making can be defined as critical to the function of society and all rely on access to these vast amounts of data and on algorithms developed to manage and process it. Thus the demand for people skilled in big data analytics and critical thinking and capable of addressing both technical and social issues in data-driven world is only going to grow.

Where in the past critical systems could be identified and defined as a diverse constellation of systems whose failure could potentially result in direct catastrophic consequences, today's critical systems are far more difficult to identify because the world is becoming every more networked, interconnected and interdependent. On the one hand this may result in more resilient systems, yet it is also far less predictable as a minor breach in one system could result in a catastrophic outcome in another. We do not aim to define what does and does not constitute a critical system. Any such definition would quickly become outdated given the rapid evolution of current technologies and an increasing interdependency between systems, networks and users. Rather we focus on an ongoing digitization of infrastructures critical to the function of democratic society and the increasing interdependency of diverse networked systems.

In their diversity these networked and interdependent systems share another underlying feature. The vast majority relies on big data and algorithms as a basis for decision-making and must confront two related but distinct problems. The first has to do with data security, addressing the vulnerabilities of networked systems that can be exploited by malicious attackers. The second and perhaps far thornier issue has to do with privacy concerns of the people whose data is collected and thus ethics of data use for decision making underlying critical functions of society. Where the first issue of data security and cyber security has received ample attention and there are many groups around the world conducting world-class research on the topic, the second issue of algorithms, privacy and ethics in the age of big data has been considered in more piecemeal fashion and requires a concerted interdisciplinary effort to address it. ITU is uniquely positioned to bring together scholars with relevant interests from a wide range of both technical and social disciplines who are already conducting research on this topic. While there are many universities in Denmark where research is conducted on big data, privacy and ethics separately, none could produce interdisciplinary discourse at the level of depth and breadth with a focus on critical functions of society that is possible at ITU. 

Thus the goal of the Critical Systems strategic research area is three-fold: 

  1. Develop a coherent research program around the core components of critical systems that we define as big data, algorithms, privacy and ethics, in the context of current ongoing and planned research and development of specific examples of critical systems such as voting, crypto currency or attestable privacy compliance methods. 
  2. Provide a truly interdisciplinary forum for knowledge exchange of current ongoing related research to enable researchers from diverse disciplines to debate, critique and take advantage of each other's expertise. 
  3. Support the development of an educational program on big data analytics that will be unique in its combination of technical, social and critical perspectives, producing students qualified to work in all areas of the Danish society that engage with data and analytics, combining technical skills and a sensitivity to the social implications of data-driven processes and practices.


Here is a more detailed description of this stategic reseach area plans.