Deep Learning Model Interpretation Towards Clarity And Confidence In Artificial Intelligence

Abstract

This research aims to analyze the interpretation of deep learning in reading and understanding data, so that artificial intelligence (AI) can assist humans in completing tasks and supporting decision making. The originality of this research lies in AI users, focusing on how deep learning interpretation through the Convolutional Neural Network (CNN) method can improve AI's ability to perform tasks and make decisions. This research uses a qualitative method with a quantitative descriptive approach through a systematic literature review. The results show that Deep Learning is a very powerful tool due to its ability to handle large amounts of data. The use of hidden layers in Deep Learning is proven to surpass the performance of traditional methods, especially in pattern recognition systems. Deep learning models are able to explain, read, and improve AI capabilities simultaneously, and absorb information effectively to improve quality decision making. The study also found that CNN contributes to building trust in AI by improving information accuracy, transparency, and detecting obstacles or anomalies, and can be applied in various fields. A good implementation will increase the confidence of healthcare workers in improving the quality of care, which in turn will reduce mortality, reduce the severity of illness, and improve the quality of life of patients, who should be encouraged to lead a healthy lifestyle