Name: Fair and Efficient Courts? A Process Mining Perspective
Start: 2026-05-12T12:45:00+02:00
End: 2026-05-12T13:30:00+02:00
Location: Carré - 2B

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Tuesday 12 May 2026 12:45 - 13:30

On May 12, we’re hosting an inspiring new session in the DSI Process Mining Series!

Fair and Efficient Courts? A Process Mining Perspective

Courts around the world are under increasing pressure—facing growing backlogs, inconsistent outcomes, and rising demands for transparency. Yet most analytical approaches tackle these challenges in isolation, missing the bigger picture. Process mining provides a unified lens to analyse fairness, delays, and transparency together. Applied to real-world court data, the talk will uncover hidden process patterns, pinpoint the true drivers of delays, and challenge assumptions about systemic bias. This talk shows how a data-driven, integrated approach can transform our understanding of court systems—and how information systems research can play a key role in shaping more efficient, transparent, and fair judicial processes.

Guest SpeakerS

Dr. Milda Aleknonyte-Resch

Dr. Milda Aleknonytė-Resch studied Economic Analysis at Vilnius University and earned a master’s degree in quantitative economics from Kiel University. She received her doctorate in Statistical Genetics from the Institute for Medical Informatics and Statistics (University Hospital Schleswig-Holstein). After completing her doctorate, she worked as a postdoctoral researcher in neurology, focusing on the analysis of medical data, and subsequently investigated time series analysis from a process mining perspective. Outside of academia, she gained experience in consulting and at a start-up, contributing to sales forecasts based on weather data.

Milda joined the Service Analytics research group as a postdoctoral researcher in August 2025 and contributes to research and teaching. Her interests include transparent approaches to data analysis in the fields of law and healthcare. She brings expertise in statistics, data science, and process mining and is particularly motivated to understand why things change over time and how AI can improve (or hinder) processes.

Dinh Thuy Nhat Vy

Dinh Thuy Nhat Vy is currently a First-year Master's student in Data Science and Artificial Intelligence Technology at Delft University of Technology. She holds a Bachelor's degree in Technical Computer Science from the University of Twente.

Her broader research interests include advanced machine learning, deep learning, and visual computing, with a focus on developing data-driven and perception-based AI systems. Within this scope, she also works on process mining and applied data science for complex socio-technical systems.

Her current research investigates fairness, efficiency, and transparency in judicial processes using process mining techniques and event log analysis. She is particularly interested in how AI and data-driven methods can support more accountable and interpretable decision-making in institutional settings.