What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components

Kacper Sokol is a postdoctoral researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), affiliated with the People-Centered Computing Lab.

Research interests

His research primarily focuses on the explainability of data-driven predictive systems based on artificial intelligence and machine-learning algorithms, with a particular interest in their applications within the medical field.