Explainable AI: Bridging the Gap Between Transparency and Model Complexity
Abstract
As Artificial Intelligence (AI) systems become more complex, ensuring their interpretability and transparency is critical for ethical and responsible deployment. This paper explores the field of Explainable AI (XAI), focusing on methods such as SHAP, LIME, and model-agnostic techniques to enhance interpretability. We analyze case studies where opaque AI models have led to biased or erroneous decisions and discuss regulatory frameworks for XAI implementation. The study concludes with best practices for balancing model performance and explainability to foster trust among stakeholders, including developers, regulators, and end users.
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References
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