Research project


Transparant Explanations for Predictive Analysis using Interactive Visualization

October 2018 - February 2024
Project Manager

The researchers in the TEPIAV project aim to empower citizens to understand the basis of all AI-driven predictive analysis that is directed at them. Though a wrong recommendation for a movie on Netflix might do little harm, in other domains such as job applications, loan decisions or medical screening, explainability is key. Being able to inspect, understand and even challenge these AI derived decisions is societally important because of their potential ramifications for individuals from a commercial, ethical and legal perspective.

The TEPAIV project will develop new interactive visualization methods for explainable AI. Key outcomes of the project will include new explainable AI methods and techniques to enable subjects to understand the results of predictive analytics and to judge the reasoning followed. Central to achieving this is the use of interactive visualization, to enable subjects to view and explore results by exploiting the unique capabilities of the human cognitive system

Key Findings

NWO TOP 612.001.752 HTI

Researchers involved in this project