Reality-centric Data Science
This Community puts the inherent and unavoidable complexity of the real world at the heart of designing, training, testing and deploying models for data analysis. The Reality-centric Data Science can operate effectively, reliably, and accountably in the real world.
We propose a reality-centric research agenda consisting of three pillars to pull together different areas of current research and build the necessary novel DS tools and models to deliver the world-changing potential of Artificial Intelligence (AI) and to answer the following questions:
- Inputs – How to model the world? How to operate with real-world data? What data to acquire? Because “data is food for AI” our actions will support the transition from a model-driven AI (collect what data you can and develop a successful model dealing with noisy data; hold the data fixed and iteratively improve the code/model) to a data-driven AI (the consistency of the data is paramount; use various tools to improve the data quality to allow multiple models to do well; hold the code fixed and iteratively improve the data)
- Outputs – How to adapt to changing circumstances post-deployment? How to respond to dynamic measures of success / performance? How to respect human constraints?
- Ecosystem – How and when to interact with humans/other systems? How to support interoperability with other components/systems?
How to get involved?
Contact the EUTOPIA curriculum team: Karen Triquet (karen.triquet@vub.be)
Learning Community Members
- Lead: Laura Dioșan (UBB). Email: laura.diosan@admin
- Partner: Octavian-Mihai Machidon (UL). Email: octavian.machidon@fri.uni-lj.si
- Partner: Mohamed Ndaoud (CY). Email: mohamed.ndaoud@essec.edu
- Partner: Harris Kyriakou (CY). Email: kyriakou@essec.edu