MediaFutures associate professor Samia Touileb will be speaker at the Winter School at the Northern Lights Deep Learning (NLDL) Conference 2026! 

The NLDL Winter School consists of tutorials by experts in the field and is co-hosted by NORA as part of the NORA Research School.

For registration, please use https://www.nldl.org/attend/registration

Getting formal ECTS Credits: UiT The Arctic University of Norway will award 5 ECTS for the Winter School to students who register formally for the course (the number of spots is limited to 40 students).  

• To register for the 5 ECTS credits use the following link: https://en.uit.no/admission#kapittel_735916

• The course description and the course code are available at: https://uit.no/utdanning/emner/emne/862218/fys-8603

• Please note that the application deadline is 15 November, which means that you will receive a confirmation of admission a week after the deadline, i.e., 25 November.

• For credits, the participants are required to present an ongoing research project (poster presentation) as part of the winter school and complete a home exam afterward. For students early in their PhD without an ongoing research project can present their PhD research objective and future project as a poster. 

• Note that the poster presentation is part of the NLDL Winter School and not part of the NLDL proceedings. The posters should be in A0 Portrait format.

• Posters can be printed locally via Xtenso. If you want to use this service, the poster should be sent to mette@xtenso.no by 20th December. The cost for poster printing is 750 NOK. 

Touilebs research focuses on alignment, bias and fairness in NLP, information extraction, summarization, and the application of NLP and machine learning in social science contexts. Her tutorial will address the main sources of bias in NLP systems, existing frameworks for fairness, safety concerns, and current debates about alignment:

While AI systems have shown remarkable progress in recent years they raise various ethical challenges. In this tutorial, we will explore this ethical dimension with a particular focus on bias, fairness, and alignment in Natural Language Processing (NLP). The tutorial will combine theoretical discussions with practical examples. We will discuss the main sources of bias in NLP systems, existing frameworks for fairness, safety concerns, and current debates about alignment. We will have a practical, hands-on, session to demonstrate how bias and other safety concerns manifest in modern NLP models, and how to critically evaluate them and reduce them.