The rapid advancement of deep learning has resulted in substantial advancements in AI-driven applications; however, the "black box" characteristic of these models frequently constrains their interpretability, transparency, and reliability. Explainable artificial intelligence (XAI) seeks to elucidate AI decision-making processes, guaranteeing that explanations faithfully represent the model's rationale and correspond with human comprehension. Despite comprehensive research in XAI, a significant gap persists in standardized procedures for assessing the efficacy and transparency of XAI techniques across many real-world applications. This study presents a unified XAI evaluation framework incorporating extensive quantitative and qualitative criteria to systematically evaluate the correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models. The framework prioritizes user-centric and domain-specific adaptations, hence improving the usability and reliability of AI models in essential domains. To address deficiencies in existing evaluation processes, we suggest defined benchmarks and a systematic evaluation pipeline that includes data loading, explanation development, and thorough method assessment. The suggested framework's relevance and variety are evidenced by case studies in healthcare, finance, agriculture, and autonomous systems. These provide a solid basis for the equitable and dependable assessment of XAI methodologies. This paradigm enhances XAI research by offering a systematic, flexible, and pragmatic method to guarantee transparency and accountability in AI systems across many real-world contexts.

本研究解决了现有可解释人工智能（XAI）技术在不同实际应用中评估标准不足的问题。我们提出了一个统一的XAI评估框架，结合定量和定性标准，系统性地评估AI模型生成的解释的正确性、可解释性、稳健性、公平性和完整性。通过在医疗、金融、农业和自动化系统中的案例研究，该框架展示了其相关性和广泛性，增强了AI系统在多个领域的透明度和可靠性。

统一框架评估可解释人工智能方法在实际应用中的有效性与透明性