Data science refers to the processing and analysis of data – in all its structured, unstructured, or multimodal varieties – to extract meaningful insights for business. Such insights are obtained through statistical procedures, scientific methods, computational techniques, experiments, and advanced machine learning and generative algorithms. Machine learning and Generative AI have become so widespread that many business decisions are now improved not only via predictive models that learn from historical data but also through autonomous agents and reasoning engines capable of complex problem-solving. While many methods and algorithms have been developed for the scientific study of data for better business decisions, there is a constant need for improving the quality and/or accuracy of decisions, adapting these methods to agentic and generative paradigms, enabling the development of new products or services, and modifying methods to improve their transparency, explainability, and human-collaborative potential.

This minitrack focuses on decision-support aspects of data science, machine learning, and Generative AI, with specific emphasis on developing novel methods or models, adapting existing methods to emerging fields such as autonomous workflows, and discovering knowledge and actionable insights. A representative list of general topic areas covered in this minitrack (not meant to be complete or comprehensive) is provided below.

New or improved methods and algorithms in data science and/or machine learning.
New or improved standardized processes and methodologies in data science.
Novel, interesting, and impactful applications of data science and machine learning for supporting better managerial decision-making processes.
Security, ethical, and privacy issues in data science and machine learning.
Explainability, interpretability, and transparency of machine learning models.
Natural language processing methodologies and innovative applications in business decision-making.
Agentic AI, autonomous agents, and multi-agent systems for automating complex business workflows.
Human-AI collaboration frameworks for teaming, delegation, and oversight in critical decision loops.
Generative AI methodologies for decision support, including RAG (Retrieval-Augmented Generation) and fine-tuned LLMs for specialized industry decisions

Extended versions of the papers accepted for presentation in this minitrack will be invited for a fast-track review and publication consideration in the Journal of Business Analytics and AI in Business [Frontiers in AI].

Minitrack Co-chairs:

Dursun Delen (Primary Contact)
Oklahoma State University
dursun.delen@okstate.edu

Behrooz Davazdahemami
University of Wisconsin – Whitewater
davazdab@uww.edu

Hamed Majidi Zolbanin
University of Dayton
hmzolbanin@udayton.edu