An Empirical Study on the Integration of Explainable Machine Learning Models in Real-World Decision-Making Systems
Keywords:
Explainable AI (XAI), interpretable machine learning, real-world decision-making, SHAP, model trust, human-in-the-loop AIAbstract
Purpose:
This study explores how explainable machine learning (ML) models can be effectively embedded into real-world decision-making systems across domains like healthcare, finance, and cybersecurity.
Design/methodology/approach:
The research adopts an empirical methodology involving case studies and simulations of ML model deployment, comparing traditional black-box models with explainable alternatives using SHAP, LIME, and decision trees.
Findings:
Explainable ML models increase user trust and adoption in high-stakes environments. They also improve error detection and compliance without significantly reducing model performance.
Practical implications:
Organizations can improve interpretability and accountability by adopting explainable models, facilitating smoother integration into decision-critical processes.
Originality/value:
This work is among the first empirical comparisons of multiple explainable ML techniques in real-world contexts and offers clear guidelines for practitioners balancing performance with transparency.
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