Artificial Intelligence (AI) is a field in computer science that attempts to reproduce traits of human intelligence in machines. Nowadays it has a significant impact in almost all human activities. However, these achievements come with a price.
First, state-of-the-art algorithms in AI demand massive computing power and energy: to handle the ever-increasing Big Data repositories, AI systems must scale in proportion to all the available data. In other words, future AI must be sustainable.
Second, the long-term use of AI systems is also threatened by human factors, above all of them, lack of trust: even achieving super-human performance, AI models are often neglected by humans, due to their lack of transparency on how they generate their predictions. In other words, future AI must be trustworthy.
Nordic Centre for Sustainable and Trustworthy Artificial Intelligence Research (NordSTAR) is an Excellent Research Environment that aims to establish a new paradigm in the research on sustainable and trustworthy artificial intelligence. The centre is led by Pedro Lind and Anis Yazidi, and is part of the OsloMet AI Lab and Applied Artificial Intelligence.
The main goal of NordSTAR is to develop AI tools, which embed all key aspects related with trustworthiness and sustainability. To do this the centre has established five research areas:
Security, safety and reliability
This research area will address data security, humans physical safety, and reliability of the communication at the level of AI methods design.
Ahmed Elmokashfi from SimulaMet (simulamet.no) leads the research area. Watch this video to learn more about security, safety and reliability:
Human factors in AI
They will incorporate the fundamental legal and moral norms underlying social behavior and consider them in the design of Sustainable and Trustworthy AI tools. This research area was initiated by Marija Slavkovik from the University of Bergen (UiB).
Elena Parmiggiani from the Norwegian University of Science and Technology (NTNU) leads this research area. Watch this video to learn more about human factors in AI:
The aim of this area is to bridge the gap between the growing number of theoretical suggestions on design and application of quantum AI and the present lack of quantitative practical results.
Sergiy Denysov leads the research area. Watch this video to learn more about quantum AI:
Biologically-inspired computational systems
They will incorporate fundamental aspects of natural intelligence in AI models, with the motivation of approaching the efficiency of biological neural systems.
Stefano Nichele leads the research area. Watch this video to learn more about biologically-inspired computational systems:
Understandable and explainable models
This research area is going to quantify the uncertainty in AI decisions and develop tools for better understanding of the different components of AI models and for explaining why specific AI decisions are obtained.
Hugo Lewi Hammer is the leader of the research area. Watch this video to learn more about understandable and explainable models:
Ahmed Elmokashfi, SimulaMet. Leader of research area: Security, safety and reliability.
Elena Parmiggiani, NTNU. Leader of research area: Human factors in AI.
Department of Journalism and Media Studies, Faculty of Social Sciences at OsloMet
At the Department of journalism and media studies, Roy Krøvel coordinates a NordSTAR satellite. Relevant research at the department is connected to illicit financial flows, digital security, fake news, social media, and more.
Oslo University Hospital
Akershus Clinical Research Center (ACR)