Explaining AI Models: The Need for Transparency in Healthcare

Explainable AI (XAI) is a crucial precondition for implementing responsible AI, ensuring that machine learning models are transparent, explainable, and accountable. Two research areas are actively addressing this problem: visual analytics and XAI. Visual analytics enables users to comprehend and interact with machine learning models by offering visualizations and tools that simplify exploration, analysis, and understanding. As a result, collaboration between these communities is becoming increasingly vital.
The article discusses the importance of explainability in AI and how it can be achieved through visual analytics and XAI. The authors highlight the imbalanced class distribution in the dataset and demonstrate that every model is trained using stratified k-fold cross-validation to ensure fairness. They then present the accuracy, loss, and F1-score of each pre-trained model, including InceptionV3, ResNet101V2, VGG19, InceptionResNetV2, and Local Interpretable Model-agnostic Explanation (LIME).
To explain complex concepts, the authors use everyday language and engaging metaphors or analogies. For instance, they compare the class weights of Normal Cell and Acute Lymphocytic Leukemia (ALL) cells to the weight of different objects in a bag, where each object represents a class. They also use visual diagrams to illustrate how the models indicate focus points in sample cell images using LIME.
In summary, the article emphasizes the significance of explainability and transparency in AI, particularly in the healthcare industry, where accurate predictions are critical. By leveraging visual analytics and XAI, researchers and practitioners can develop more accountable and reliable AI systems that can be trusted by both professionals and laypeople alike.