Deep learning applications have garnered significant attention due to their superior performance in tasks like image and speech recognition, as well as recommendation systems, surpassing human capability in many areas. However, these applications often lack explainability and reliability, as deep learning models are typically seen as black boxes with complex underlying mechanisms. These models do not provide justifications for their decisions and predictions, leading to a lack of trust from humans. This lack of trust is particularly concerning in scenarios like autonomous driving, where errors could have catastrophic consequences. Similarly, in the medical field, the decisions made by AI algorithms can impact human lives significantly.
To address these issues, a variety of methods have been developed in the field of XAI (Explainable Artificial Intelligence). These methods aim to provide explanations for deep learning models that are easily interpretable by humans. These explanations can take the form of visual, textual, or numerical formats. Visual interpretability methods, for example, include visual explanations and plots that highlight the important features contributing to a model’s decision. One popular example of this is Class Activation Mapping (CAM), a saliency method that visualizes the features responsible for a model’s classification decision.
Another approach is Gradient-weighted Class Activation Mapping (Grad-CAM), which extends the CAM method by using gradients to provide more detailed spatial information about the important features in an image. Layer-Wise Relevance Propagation (LRP) is another visual explanation technique that decomposes the decision-making process in a neural network to provide relevance scores for each neuron’s input. Peak Response Maps (PRM) are introduced for weakly supervised instance segmentation to identify class activations and generate peak response maps.
CLass-Enhanced Attentive Response (CLEAR) is a method that visualizes decisions of deep neural networks using activation values. Similarly, CLass-Enhanced Attentive Response (CLEAR) generates interpretable confidence scores for each class and builds attention maps based on individual response maps and dominant class attentive maps. For features activation visualization, Deconvolutional Networks are used to reverse the feature maps into approximate images.
DeepResolve is a method that uses feature maps from intermediate layers to understand how a network combines these features to classify an input image. The system creates feature importance maps and maps the response of each class and the dominant class attentive maps. A similar approach, SCOUTER, uses a slot attention-based classifier to generate explanations for image recognition.
Visual Question Answering (VQA) frameworks employ co-attention mechanisms to attend to both image regions and question words for generating answers. This involves generating co-attentions at different hierarchy levels, such as word-level, phrase-level, and question-level co-attention maps.semantic information is used to interpret deep neural networks for video captioning tasks. While XAI frameworks like INNvestigate Neural Networks and explAIner provide tools and methods to interpret machine learning models easily.
In applications like autonomous driving and healthcare, explainable systems are being developed to improve the decision-making processes of AI models. In autonomous driving, AI systems are being guided by human observations to make informed decisions, while in healthcare, XAI methods are being used for disease diagnosis and detection, such as detecting coronavirus from X-ray images.
Overall, the field of Explainable AI (XAI) continues to evolve, with a focus on making AI systems more interpretable and trustworthy for real-world applications. Researchers and practitioners are leveraging a variety of methods and frameworks to enhance the transparency and explainability of deep learning models, leading to safer and more reliable AI systems in critical domains.