Explainable Artificial Intelligence (XAI): Enhancing Transparency and Trust in Intelligent Decision Systems
DOI:
https://doi.org/10.64235/bykyfe31Keywords:
Explainable artificial intelligence, interpretable machine learning, algorithmic transparency, model-agnostic explanations, responsible AIAbstract
The rapid proliferation of artificial intelligence (AI) systems across high-stakes domains including healthcare, finance, criminal
justice, and autonomous systems has amplified concerns regarding algorithmic opacity and the consequent erosion of human
trust. Explainable Artificial Intelligence (XAI) represents a multidisciplinary research paradigm aimed at rendering AI decision
processes interpretable, transparent, and accountable to human stakeholders. This study provides a comprehensive examination
of the current state of XAI research, evaluating prominent explanation methods—including LIME (Local Interpretable Modelagnostic
Explanations), SHAP (SHapley Additive exPlanations), gradient-based attribution techniques, and attention mechanisms—
across multiple benchmark datasets spanning medical imaging, financial risk assessment, and natural language processing.
Employing a mixed-methods research design, we assessed explanation quality using quantitative fidelity metrics and qualitative
user comprehension studies. Results indicate that SHAP achieves the highest composite fidelity score (88.1%) among evaluated
methods, while gradient-based approaches demonstrate superior scalability in high-dimensional settings. User studies reveal
that model-agnostic explanations significantly improve perceived trust and decision confidence among domain experts (p < .001).
Critically, a persistent tension between model performance and interpretability is identified across all experimental conditions.
The findings carry important implications for the design of responsible AI systems and inform regulatory frameworks currently
under development globally. Future research directions encompassing causal XAI, real-time explanation generation, and culturally
adaptive explanation interfaces are delineated.
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