See

our projects

We develop AI technology that highlights the inequality of opportunity in society and that actively increases the prospect of equality of opportunity in education, well-being, environment, mobility and health. We also serve as an information point for residents and businesses who have questions about new AI technologies and the ethical and inclusive use of them.

⇣ PhD projects

⇣ MSc projects

⇣ BA projects

PhD projects

Maximizing fair resource distribution in education

This project focuses on the development and deployment of new tools for fair distribution of educational finances. The tools rely on machine learning algorithms that allow professionals that are not experts in machine learning and statistics, to define unwanted machine behavior, such as unequal treatment of certain population groups by automatic decision systems.

Equalizing access & treatment in healthcare

In this project we develop machine learning models for differentially fair classification and (causal) prediction tasks in healthcare, that take into account social factors and fundamental human rights such as non-discrimination and equality and privacy. Unlike most other fair machine learning models in healthcare, which focus mathematical notions of fairness, models developed in this work package build on intersectionality theory.

Identifying actionable explanations in well-being

This project develops models whose predictive outcomes are explainable and adaptable at the same time. These so-called actionable explanations make it possible for end users, such as well-being professionals, to identify useful variables (such as income) that can be used to steer towards certain desired outcomes (such as weight reduction). We combine the appeal of counterfactuals explanations with the properties of actionable features to generate actionable explanations.

Predicting socio-economic

inequalities from street

This project develops algorithms for uncovering and explaining (the emergence of) inequality in the city based on panorama images of the city. Explainable deep vision models will be combined with case-based-learning models to predict traditional inequality scores, to detect and explain image elements predictive of inequality and to prevent the emergence of urban spaces with an increased risk of inequality.

Tackling mobility poverty using public & citizen data

The project focusses on machine learning algorithms to 1) extract and combine mobility patterns from various data sources such as citizen data, household information, and other publicly accessible geodata such as OV data and Taxi data, 2) uncover economic as well as social and cultural drivers of spatial divisions, segregation and inequality in mobility, 3) to recommend equality-enhancing and poverty-reducing mobility flows.

Public Goverance

Assessing Errors and Supporting Fair AI in the Public Sector

The focus of this research is to develop practical tools and methods for error assessment to support the fair use of AI in the public sector and is conducted in collaboration with the Dutch Ministry of Interior Affairs.

Ethics and Law

Inclusive and rights-based norm design for AI systems

Striking the right balance between artificial intelligence (AI) and algorithmic systems development and protecting and promoting human rights and freedoms is a key concern of the current debate. There is a significant gap between envisioning rights-based AI systems and their practical application. This research aims to address that gap.

MSc projects

The Effect of Link Recommendation Algorithms on Groups’ Opinion Visibility in Online Social Networks

This project plans to develop an opinion dynamics model to infer how link-recommendation algorithms might impact the possibility that different groups widely spread their opinion.

Information spread in social networks

The current project aims to understand the predictors of the virality of a tweet in a user network.

Adapting Public Transport for a (Post)Pandemic World

This research aims to find an intelligent way to reduce urban public transport networks while maintaining equal access equality across the city using reinforcement learning.

Fairness in Mobility and Facility Location through Clustering

This project tries to answer the question of fairness in regards to access to certain points of interest in cities, measured for different social groups of people, such as low versus high-income citizens.

Candidate Profile Synthesis for Bias Mitigation in Job Candidate Recommendation.

Many scalable, automated hiring systems are prone to harming and discriminating against certain groups; this project addresses this issue by tackling it at its core: the synthesis of fair, unbiased training data.

Improving fairness in the lottery matching system used in Dutch secondary admission by realistic synthetic data

The project seeks to improve fairness in the lottery matching system used for secondary school admission in Amsterdam, by generating realistic synthetic data about Dutch students’ school choice preferences which are essential for training the matching system.

Predictive modeling for the food environment in Amsterdam.

Analysing how practical counterfactual methods are for interpreting ML models

This master thesis aims to analyze the practicality of two counterfactual methods to make Machine Learning models interpretable for non-technical users

BA projects

Mapping and Predicting Inequality based on Street Imagery

This project investigated to what extent environmental inequalities in Amsterdam are measurable from panoramic street imagery by replicating the study of Suel et al. (2019) and study their model transferability to data of Amsterdam. Model transferability was studied by applying pre-trained weights from Suel et al. (2019) and by training their model solely on Amsterdam data. Performance was evaluated by comparing outcome values to data from official statistics.