Exploring the Trade-Offs Between Blackbox and Explainable AI: A Comparative Study

Abstract

Healthcare, finance, and transportation are just a few of the sectors where artificial intelligence (AI) is now a necessity. AI algorithms come in two flavors: explainable and blackbox. Explainable models are intended to be more transparent and interpretable than blackbox models, which are complicated and challenging to understand. Through a comparative analysis, this article investigates the trade-offs between explainable AI and blackbox AI. It specifically looks into how explainability affects accuracy, user perception of AI models, the efficiency of explainability methods, domain-specific effects, and ethical ramifications. The study seeks to offer useful insights into the advantages and disadvantages of these models and to assist in guiding decisions regarding the use of explainable and blackbox AI in various applications.

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