Visualizing Uncertainty: Spatial Maps of Missing and Conflicting Evidence in Deep Learning

Deep neural networks' reliability in safety-critical domains hinges on understanding their uncertainty, which existing methods only partially address by providing scalar confidence measures. A new framework visualizes uncertainty in spatial maps, highlighting areas of input data that contribute to different types of uncertainty, such as missing or conflicting evidence¹. This approach enables a more nuanced understanding of model confidence, allowing practitioners to identify and address specific regions of uncertainty. By doing so, developers can refine their models to improve performance and reliability in high-stakes applications. The ability to visualize and quantify uncertainty has significant implications for the deployment of machine learning systems in areas like healthcare, finance, and transportation. So what matters to practitioners is that this framework can help them build more transparent and trustworthy models, which is crucial for ensuring the safe and effective use of deep learning in critical domains.