1 code implementation • 1 Feb 2024 • Aditya Bhattacharya, Simone Stumpf, Lucija Gosak, Gregor Stiglic, Katrien Verbert
Explanations in interactive machine-learning systems facilitate debugging and improving prediction models.
no code implementations • 3 Oct 2023 • Aditya Bhattacharya, Simone Stumpf, Lucija Gosak, Gregor Stiglic, Katrien Verbert
Our research involved a comprehensive examination of the impact of global explanations rooted in both data-centric and model-centric perspectives within systems designed to support healthcare experts in optimising machine learning models through both automated and manual data configurations.
no code implementations • 24 Jul 2023 • Gregor Stiglic, Leon Kopitar, Lucija Gosak, Primoz Kocbek, Zhe He, Prithwish Chakraborty, Pablo Meyer, Jiang Bian
The time needed to answer questions related to the content of abstracts was significantly lower in groups two and three compared to the first group using full abstracts.
no code implementations • 21 Feb 2023 • Aditya Bhattacharya, Jeroen Ooge, Gregor Stiglic, Katrien Verbert
Results indicate that our participants preferred our representation of data-centric explanations that provide local explanations with a global overview over other methods.
no code implementations • 2 Jun 2020 • Simon Kocbek, Primoz Kocbek, Leona Cilar, Gregor Stiglic
Machine Learning (ML) models are often complex and difficult to interpret due to their 'black-box' characteristics.
no code implementations • 20 Feb 2020 • Gregor Stiglic, Primoz Kocbek, Nino Fijacko, Marinka Zitnik, Katrien Verbert, Leona Cilar
There is a need of ensuring machine learning models that are interpretable.