More sustainable,
risk-averse and ethical AI technologies
One of the six national AI research centres.
Creating a new generation of algorithms
SURE AI is a research center that builds the mathematics behind trustworthy AI—so safety, fairness, and sustainability are not just goals, but proven properties.
We advance core theory in probability, statistics, optimization, and control to model uncertainty and risk, then turn that theory into algorithms with guarantees on accuracy, robustness, and resource use.
Partnering with industry and the public sector, we convert these foundations into open tools and benchmarks, tested in real-world applications, and high-quality training that set the standard for responsible AI in practice.
News
Tracking the unseen: how topological Kalman filters reveal hidden network dynamics
May 2026
Research
Read more
Unifying Risk and Belief: A Foundation for Coherent AI Decision-Making
October 2025
Research
Read more
The economics of overlapping generations: a stochastic lens
October 2025
Research
Read more
Toward trustworthy AI: recovering reliable decisions from corrupted and uncertain data
May 2026
Research
Read more
Risk-averse ensemble control: why "average" performance is no longer good enough
May 2026
Research
Read more
The Geometry of Data - Why Machine Learning Needs Signatures
February 2025
Research
Read more
SURE-AI kicks off: “of vital importance to our future”
December 2025
News
Read more
Beyond the Monolith: How Brain-Inspired AI is Building a More Reliable and Sustainable Future
November 2025
Research
Read more
Signals with shape: why topology matters for modern data?
November 2025
Research
Read more
Why horizon matters more than you think in decision making
November 2025
Research
Read more
The economics of overlapping generations: a stochastic lens
October 2025
Research
Read more
Unifying Risk and Belief: A Foundation for Coherent AI Decision-Making
October 2025
Research
Read more
SURE-AI at seminar on the sustainabiliy of AI
Tracking the unseen: how topological Kalman filters reveal hidden network dynamics
Toward trustworthy AI: recovering reliable decisions from corrupted data
Risk-averse ensemble control: why "average" performance is no longer good enough
The Geometry of Data - Why Machine Learning Needs Signatures
SURE-AI kicks off: “of vital importance to our future”
Beyond the Monolith: how brain-inspired AI is building a more reliable and sustainable future
Signals with shape: why topology matters for modern data?
Why horizon matters more than you think in decision making
The economics of overlapping generations: a stochastic lens
Unifying Risk and Belief: A Foundation for Coherent AI Decision-Making
- Sustainable AI means building and using AI in a way that’s good for people and the planet over the long run. It keeps energy, water, and hardware use low and is affordable to run and maintain. In practice, that means building efficient models with a low environmental impact. This is a central topic for the SURE AI research center.
- Risk-averse AI means building systems that avoid unnecessary risk and harm, and that are robust to handle challenges and scale without breaking. They know when they’re unsure, choose the safer option, and ask a human when needed. We test them thoroughly, set clear limits, and add backups so they fail safely and keep mistakes small.
- Ethical AI means building human values right into the algorithms, so they’re ethical from the inside out. Ethical AI means building systems that treat people fairly and with respect. They protect privacy, use data responsibly, and avoid bias, with clear rules about what the AI can and cannot do. We keep choices transparent, explain decisions in plain language, and make sure people—not machines—stay accountable and in control.
Our partners
The Centre includes international researchers from the following institutions
Our partners
Contact
+47 999 99 999
mail@mail.com
Adress 15, 5670 Oslo
Navigation
Research
Team
Educational
News
Events
Privacy Policy
Other information
© Copyright SURE-AI 2025
Our partners
The Centre includes international researchers from the following institutions
