
Explainability in AI Systems
MICRO-DEGREEThis course addresses the challenges of building ethical and transparent AI-backed systems across various sectors.
Ekaterina Artemova is a researcher specializing in natural language processing (NLP), with a focus on the post-training and evaluation of large language models (LLMs). She earned her degree from the Higher School of Economics (HSE) and completed a postdoctoral fellowship at LMU Munich. Her research interests include artificial text detection, neural architecture search, and multilingual language understanding benchmarks. Some of her notable contributions involve studies on non-English languages, the topology of attention maps in NLP models, and advancements in benchmarking methodologies.

This course addresses the challenges of building ethical and transparent AI-backed systems across various sectors.

This module offers an introduction to the current state-of-the-art techniques in the area of Natural Language Processing (NLP), namely Large Language Modules (LLMs).

Students explore advanced data analysis for decision-making, covering key analytics types and their business applications through both theory and practice.
Professor
Head of Research Group "Data-Centric Language Technologies"
Data-Centric Language Technologies