Human-Centric Machine Learning: Transparency, Explainability, and Guidance by Human Feedback

Name: Human-Centric Machine Learning: Transparency, Explainability, and Guidance by Human Feedback
Start: 2026-05-07T17:15:00+02:00
End: 2026-05-07T18:30:00+02:00
Location: Campus Oerlikon, Binzmühlestrasse 14, 8050 Zürich, BIN 2.A.01

Schedule

Thu May 07 2026 at 05:15 pm to 06:30 pm

UTC+02:00

Location

Campus Oerlikon, Binzmühlestrasse 14, 8050 Zürich, BIN 2.A.01 | Zurich, ZH

Many of the machine learning models used today exhibit black-box behavior: human users cannot easily interpret the internal mechanisms and decision-making processes. And although these models have been trained with huge amounts of data, they rarely take into account the specific needs of users.
This talk showcases methodological advances in human-centered machine learning to control model behavior, reduce bias, and improve alignment with user requirements.
Speaker: Matthias Zeppelzauer, University St. Pölten
This talk is part of the public IfI Colloquium series: https://www.ifi.uzh.ch/en/studies/phd/colloquium/spring-2026.html#Zeppelzauer