Developing Interpretable Models for Complex Decision-Making

Developing Interpretable Models for Complex Decision-Making

Background: Due to the rising complexity of decision-making processes, there is a growing need for machine-learning models that can be easily understood. Ensuring the ethical use of AI in different fields requires establishing trust and understanding in the results generated by models. Objective: Th...

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Bibliographic Details
Main Authors: Volodymyr Mikhav, Mohammed Ahmed Shakir, Haithem Kareem Abass, Ola Farooq Jelwy, Husam Najm Abbood Al-Bayati, Salman Mahmood Salman, Nataliia Bodnar
Format: Article
Language:English
Published: FRUCT 2024-11-01
Series:Proceedings of the XXth Conference of Open Innovations Association FRUCT
Subjects:
interpretable models; complex decision-making; machine learning transparency; explainable ai (xai); decision trees; feature importance; predictive analytics; human-in-the-loop ai
Online Access:https://www.fruct.org/publications/volume-36/fruct36/files/Ahm.pdf

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https://www.fruct.org/publications/volume-36/fruct36/files/Ahm.pdf

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