Projects

The objective of the AI Oversight Lab is to enable public entities to develop and use AI tools, while adhering to ethical principles and respecting human rights, and to facilitate oversight bodies in their auditing role.

Improve the matching between vacancies from companies and job seekers by developing an AI demonstrator that provides vacancy scores and recommendations of skills changes.

In this project we focus on the key factors of success and failure of implementing risk assessment tools in the justice domain, and identify technical, socio-technical, and social solutions to common problems.

Identifying heart failure patients at high risk using Multi-Party Computation.

Integrating human knowledge and AI techniques for better diagnosis and effective predictive maintenance of high tech systems.

The purpose of DASH is to allow humans to efficiently tell an AI-based system what to do without having to spell out all details, and without losing meaningful human control over that system.

The Horizon Scanner can gather information from online sources, and provide and visualize potential trends.

Integrating Human Knowledge in the form of Attention in a Deep Learning Model

Within this project AI-based building models are developed to solve the problem of peak loads in energy demands caused by renewable energy sources.

The Learning with Less Labels (LwLL) program aims to make the process of training machine learning models more efficient by reducing the amount of labeled data required to build a model by six orders of magnitude, and by reducing the amount of data needed to adapt models to new environments from tens to hundreds of labeled examples. This program is funded by the Defense Advanced Research Projects Agency (DARPA).

Making autonomous public transport aware of what is required at any moment for safe travel.

In this project, we use text mining techniques to mine forum data and create a search system that allows users to get insight in forum posts (either Dutch or English)

Enabling survival analyses on privacy-sensitive distributed cancer data using Multi-Party Computation

Individually, major banks are facing significant challenges with respect to detecting money laundering. Due to privacy regulations and data confidentiality banks cannot readily share data, which makes it challenging to collaborate. The goal of this joint research project is to develop a Proof of Concept Multi-Party Computation solution for collaborative monitoring and detection of money laundering.

Predicting the anthropogenic causes of subsidence in the Netherlands and unravel the contributions of deep versus shallow processes.

In this work we have investigated how a deep learning model can be fooled into misdetecting large objects like an aircraft by placing a relatively small sticker on top of the object.