9 citations · 12 across the 14 of their papers we have counts for
15 papers
From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch
Laurens Samson, Iva Gornishka, Gossa Lô +2
Large language models are increasingly being deployed in governmental settings, yet few existing evaluation frameworks jointly reflect the values of public administration and the l…
AI From the Margins (AIM): Rethinking Participatory AI Design Through the Lived Experience of Minoritized Communities
Tijs Portegies, Laureanne Willems, Maaike Harbers +5
Artificial intelligence (AI) can reproduce and amplify the structural inequities faced by minoritized communities. Participatory AI has been proposed as a response, but participati…
Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning
Dimitris Michailidis, Sennay Ghebreab, Fernando P. Santos
We tackle the Metro Network Expansion Problem (MNEP), a subset of the Transport Network Design Problem (TNDP), which focuses on expanding metro systems to satisfy travel demand. Tr…
From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Health
Sara Altamirano, Tijs Portegies, Sennay Ghebreab
Algorithmic fairness is essential for responsible ML-driven public health research, yet its practical implementation remains limited. To investigate this awareness-action gap, we c…
Inequality in Congestion Games with Learning Agents
Dimitris Michailidis, Sennay Ghebreab, Fernando P. Santos
Who benefits from expanding transport networks? While designed to improve mobility, such interventions can also create inequality. In this paper, we show that disparities arise not…
Same Content, Different Answers: Cross-Modal Inconsistency in MLLMs
Angela van Sprang, Laurens Samson, Ana Lucic +3
We introduce two new benchmarks REST and REST+ (Render-Equivalence Stress Tests) to enable systematic evaluation of cross-modal inconsistency in multimodal large language models (M…
Machine Learning and Public Health: Identifying and Mitigating Algorithmic Bias through a Systematic Review
Sara Altamirano, Arjan Vreeken, Sennay Ghebreab
Machine learning (ML) promises to revolutionize public health through improved surveillance, risk stratification, and resource allocation. However, without systematic attention to…
How large language models judge and influence human cooperation
Alexandre S. Pires, Laurens Samson, Sennay Ghebreab +1
Humans increasingly rely on large language models (LLMs) to support decisions in social settings. Previous work suggests that such tools shape people's moral and political judgemen…
EMERGENT: Efficient and Manipulation-resistant Matching using GFlowNets
Mayesha Tasnim, Erman Acar, Sennay Ghebreab
The design of fair and efficient algorithms for allocating public resources, such as school admissions, housing, or medical residency, has a profound social impact. In one-sided ma…
Artifacts of Idiosyncracy in Global Street View Data
Tim Alpherts, Sennay Ghebreab, Nanne van Noord
Street view data is increasingly being used in computer vision applications in recent years. Machine learning datasets are collected for these applications using simple sampling te…
The Cloud Weaving Model for AI development
Darcy Kim, Aida Kalender, Sennay Ghebreab +1
While analysing challenges in pilot projects developing AI with marginalized communities, we found it difficult to express them within commonly used paradigms. We therefore constru…
EMPLACE: Self-Supervised Urban Scene Change Detection
Tim Alpherts, Sennay Ghebreab, Nanne van Noord
Urban change is a constant process that influences the perception of neighbourhoods and the lives of the people within them. The field of Urban Scene Change Detection (USCD) aims t…
Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
Dimitris Michailidis, Willem Röpke, Diederik M. Roijers +2
Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. MORL is computationally more…
Privacy-Aware Visual Language Models
Laurens Samson, Nimrod Barazani, Sennay Ghebreab +1
As Visual Language Models (VLMs) become increasingly embedded in everyday applications, ensuring they can recognise and appropriately handle privacy-sensitive content is thus essen…
Analysing and Organising Human Communications for AI Fairness-Related Decisions: Use Cases from the Public Sector
Mirthe Dankloff, Vanja Skoric, Giovanni Sileno +3
AI algorithms used in the public sector, e.g., for allocating social benefits or predicting fraud, often involve multiple public and private stakeholders at various phases of the a…