An Explainable Artificial Intelligence Framework for Improving Decision-Making Accuracy and Transparency in Complex Real-World Applications
DOI:
https://doi.org/10.66592/fgd245seKeywords:
Explainable artificial intelligence; interpretable machine learning; SHAP; LIME; counterfactual explanations; decision support; calibration; transparency; human oversight.Abstract
Complex decision-support systems increasingly rely on high-capacity machine learning, yet predictive performance alone cannot guarantee transparent, accountable or useful decisions. This paper proposes a modular explainable artificial intelligence (XAI) framework combining calibrated risk prediction, local and global feature attribution, counterfactual recourse, explanation-quality testing, drift surveillance and human review. A reproducible evaluation protocol is specified for heterogeneous real-world settings. To illustrate the planned analysis without claiming an executed experiment, numerical tables and charts report explicitly hypothetical demonstration values. The proposed architecture separates model accuracy from explanation fidelity and decision utility, allowing each to be tested independently. The paper identifies governance controls, operational limitations and falsifiable hypotheses for future empirical validation.
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