MLUX Case Study: Designing AI Explainability Features with Milda Norkute
Event details: bit.ly/mlux0421chi
MLUX Case Study: Designing AI Explainability Features
What is AI explainability and why would we want to build explainability features into AI systems we are creating? Milda will share her work and learnings from designing an explainability feature to the implementation of a legal text summarization solution based on a Deep Learning (DL). The findings provide insights into the benefits and the challenges of selecting suitable mechanisms to provide explainability for DL models.
Join us on April 28th, 6pm CET / 9am PT, for a case study on Designing AI Explainability Features with Milda Norkute from Thomas Reuters Labs in Switzerland!
About Milda Norkute
Milda is a Senior Designer working at Thomson Reuters Labs in Zug, Switzerland. She works closely with data scientists and engineers on enhancing products and services across Thomson Reuters product portfolio (incl. Reuters News, Westlaw, HighQ, Onvio and others) with Artificial Intelligence (AI) solutions. Milda is focused on user research and design of the concepts to figure out how and where to put the human in the loop in AI powered systems. Before joining Thomson Reuters Milda worked at Nokia, designed and built tools for scientists at CERN.