Abstract: The growing use of machine learning in data-driven organizations has increased the need for models that are not only accurate, but also interpretable, reliable, and operationally meaningful. Particular attention is given to decision environments shaped by complex organizational data, where transparency and robustness play a critical role in the quality of managerial actions. Within this context, machine learning is addressed as a core component of decision intelligence supported by analytical reasoning, domain knowledge, and implementation constraints. This perspective establishes a connection between interpretable machine learning and trustworthy decision intelligence in management information systems.

Biography: Prof. Dr. Filiz Ersöz is a faculty member in the Department of Management Information Systems at OSTIM Technical University. Over the course of her academic career, she has worked in the fields of statistics, operations research, industrial engineering, software engineering, and knowledge engineering. Her research focuses on data science, machine learning and artificial intelligence, data mining, simulation and modelling, multi-criteria decision-making, and decision support systems. She conducts interdisciplinary studies that integrate analytical methods, intelligent systems, and data-driven approaches to support modelling, evaluation, and decision processes across different application areas.