Artificial intelligence systems using deep learning have achieved image and speech recognition at performance levels on par with human beings.However, an issue is not being able to understand the basis on which a deep learning system determines its output. In this talk, we introduce Attention Branch Network (ABN), which outputs attention, an area that deep learning focuses on when determining inferential results. ABN is a deep learning network that can contribute to improving recognition performance while acquiring the attention mechanism. As application examples of ABN, we introduce ABN’s visual explanation of automated driving and medical diagnosis decisions. Visualization of attention here means being able to view the AI’s direction of attention. This technology holds great promise as an approach for interpreting the basis for decisions output by an AI system.