Explainability in AI Systems
MICRO-DEGREEThis course addresses the challenges of building ethical and trans-parent AI-backed systems across various sectors, introducing explainability techniques in AI and machine learning (explainable AI, XAI).
About
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.
Coordinated Study Programs
Natural Language Processing
MICRO-DEGREEAn introduction to the current state-of-the-art techniques in the area of Natural Language Processing (NLP), namely Large Language Modules (LLMs), which form the core of modern Artificial Intelligence (AI).
Data Analytics and Decision-Making
MICRO-DEGREEThis module provides advanced knowledge about the struc-ture and analysis of large amounts of data and how this is to be used in decision-making processes.
Quick Info
Position:
Professor
Role:
Head of Research Group "Data-Centric Language Technologies"
Teaching Subject:
Data-Centric Language Technologies