most citedPrivacy-Aware Visual Language Models

2 citations · 2 across the 2 of their papers we have counts for

collaborators

14 papers

cs.CL2026

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…

cs.CV20262 cited

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…

cs.LG2026

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…

cs.CY2026

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…

cs.CY2026

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…

cs.AI2026

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…