Role of the AI in Deep Learning Applications
DOI:
https://doi.org/10.15662/IJARCST.2021.0406004Keywords:
Explainable Artificial Intelligence, Deep Learning, Model Interpretability, Saliency Maps, SHAP, LIME, Transparency, Ethical AI, Model-Agnostic Methods, Decision TrustAbstract
Explainable Artificial Intelligence (XAI) is an emerging field that seeks to enhance transparency and interpretability in AI systems, particularly deep learning models that are often regarded as "black boxes." While achieving state-of-the-art performance in a variety of applications, including image recognition, natural language processing, and medical diagnostics, deep learning models are notoriously difficult to understand because of their complex architectures and high-dimensional feature spaces. The lack of interpretability poses challenges for trust, ethical deployment, regulatory compliance, and decision-making in critical domains. This abstract serves to outline the role of XAI in deep learning applications, bridging the gap between high-performance models and their comprehensibility. These techniques, including saliency maps, Layer-wise Relevance Propagation (LRP), and SHAP (SHapley Additive exPlanations), contribute to the explanation of model predictions by attributing importance to input features. The model-agnostic approach, such as LIME (Local Interpretable Model-agnostic Explanations), provides additional flexibility by decoupling interpretability from specific architectures. XAI allows stakeholders, such as data scientists, domain experts, and end-users, to understand model behavior, identify possible biases, and improve model robustness. Especially in sensitive areas like healthcare and finance, explainability ensures that AI-driven decisions are transparent, promoting accountability and trust among users. Moreover, regulatory frameworks like the General Data Protection Regulation by the European Union are increasingly asking for human-interpretable explanations from AI systems. By advancing interpretability without losing accuracy, XAI enables the ethical and responsible deployment of deep learning models in real-world scenarios. Further research in this area is required to finally get transparent, reliable, and fair AI systems
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