Antwerp Center on Responsible AI

ACRAI Research Day

19 March 2026, Building O, Aula O2 Campus Drie Eiken, Universiteitsplein 1, 2610 Wilrijk

 Antwerp Center on Responsible AI (ACRAI) Research Day

The Antwerp Center on Responsible AI (ACRAI) invites researchers, academics, industry experts, policymakers, and students to the ACRAI Research Day - a platform for exploring advancements in Responsible AI. This event is designed for professionals and scholars across AI, data science, ethics, law, and related fields who wish to deepen their expertise, present findings, and collaborate on solutions to the ethical and societal challenges posed by AI.

Date and Location

The ACRAI Research Day will be held on Thursday, 19th March 2026, at the Campus Drie Eiken, Building O Aula O2.

Research Day Schedule

Preliminary schedule: 

TimeContent

9:30 AM - 10:00 AMRegistration + Coffee
10:00 AM - 10:15 AMWelcome: Prof. David Martens & Prof. Toon Calders 
10:15 AM - 11:30 AMPresentations (1)
11:30 AM -12:00 PMFlash Talks Poster session
12:00 PM - 1:15 PMLunch + Poster Session
1:15 PM - 2:30 PMPresentations (2)
2:30 PM - 3:30 PMResearch Speeddating + Coffee
3:30 PM - 4:30 PMPresentations (3)
4:30 PM - 6:00 PMReception

Practical Information


Oral Presentations
Presentations (1)
Presentations (2)
Presentations (3)
Manon Reusens
(UAntwerpen)
Would a Large Language Model Pay Extra for a View? Inferring Willingness to Pay from Subjective Choice
Tom Vandeuren 
(UAntwerpen)
From Prediction to Prescription: A Data-Driven Approach to Athletic Training
Stig Hellemans
 (UAntwerpen)
Building a production-ready de-identification system for sensitive clinical texts
Arian Sabaghi Khameneh 
(UAntwerpen)
Measuring Explanation Uncertainty in Autonomous Navigation Systems
Kimberly Van Sande
When should 'things' become taxpayers?
Justin Young
 (UAntwerpen)
AI and the Future of Professional Writing
Benjamin González 
(UAntwerpen)
Anticompetitive Effects of Algorithmic Targeting
​Oberon De Deurwaerder (UAntwerpen)
​SLM for generating synthetic training data in a reinforced supervised learning setup​
Romi Vandoren 
(UAntwerpen)
The impact of information bleed and negative data bias on AI model performance for the TCR-epitope prediction problem
Arne Vanhoywegen
(VUB)
GPT-5.1 Performing Human-Aligned Grading in Hand-Written Calculus Tests
Jelle Vekeman
(UAntwerpen)
​Rethinking the balance between accuracy and computational cost in computational chemistry: the case of machine learning potentials​
Fawaz Sammani
(VUB)
Concept Insertion in Diffusion Models via Prompt Switching
Victor De Marez
Assessing factual sycophancy in large language models
Mateusz Cedro
(UAntwerpen)
​Cash or comfort? How LLM's value our everyday inconveniences​


Poster Presentations (A1 format)
Presenter
Title
Marco Favier 
(UAntwerpen)
Cherry On The Cake: Fairness is NOT an optimization problem
Nick Wils 
(UAntwerpen)
Fairness in epdemiological hypergraphs
Eleni Gkovedarou 
(UAntwerpen)
Gender-Fair Rewriter for English-to-Greek Translations
Farnaz Faramarzi Lighvan 
(VUB)
Adaptive Weighted LSSVM for Multi-View Classification
Francesca Carlon 
(VUB)
Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods.
Kauãn Steurs & Aäron Bingé
(UAntwerpen)
The ultimate design machine (interactive)
Wannes Slembrouck & Jolan Dijkmans
(UAntwerpen)
Rethinking right tool at the right time for CAD learning
Malek Ferjani
(UAntwerpen)
One definition, big consequences: what counts as “transparency” in EU AI governance?
Yifan He 
(UAntwerpen)
An Agentic Approach to Generating XAI-Narratives
Ine Gevers 
(UAntwerpen)
In Benchmarks We Trust … Or Not?
Berre Deltomme 
(UAntwerpen)
New Approaches to Behavioral Measurement using Large Language Models: Opportunities and Risks for Responsible AI in Survey-Based Research
Charlotte Adams 
(UAntwerpen)
Reference bias in clinical proteomics: Do current proteome references fail underrepresented groups?
Siem Buseyne
(UAntwerpen)
​How system autonomy shapes teachers’ acceptance of AI‑supported educational technology: Evidence from a quantitative vignette study. 
Magali Legast
(UAntwerpen)
No evaluation without fair representation : Impact of label and selection bias on the evaluation, performance and mitigation of classification models