# Explainable Artificial Intelligence: Advancements and Limitations
## 5 Conceptual Explanations  
While visual explanations by activation maximisation and pixel attribution techniques are widely employed for computer vision tasks, there is also a growing interest in conceptual forms of explanations. Methodologies proposed in this direction aim to provide a high-level understanding of how models make decisions by linking their internal representations to human-interpretable concepts. This is particularly useful when the goal is to explain complex models in terms of concepts or features that align with human reasoning. Network dissection [79] is a systematic methodology for interpreting the internal representations of neural networks by quantifying how individual neurons encode specific human-interpretable concepts. It provides a way to analyse the emergent structure within deep networks, particularly CNNs. The technique involves mapping the activation patterns of neurons to a predefined set of semantic concepts derived from labelled datasets, such as object categories, textures, or scenes. By evaluating the alignment between neuron activations and these concepts, network dissection enables researchers to understand the role and specialisation of neurons in the decision-making process of a model. This method is significant in demystifying the \"black-box\" nature of neural networks, providing insights into their transparency and interpretability, which are critical for debugging, enhancing trustworthiness, and identifying biases in AI systems. Despite these advantages, network dissection has significant limitations. A key drawback is its reliance on segmentation maps for concepts, which are costly and time-consuming to create, especially for large and diverse datasets. This dependence can introduce biases and restrict the scope of analysis to predefined, segmented concepts, potentially overlooking emergent or complex features not present in the available segmentation maps. Additionally, network dissection focuses on individual units, which may fail to account for the collective behaviour of multiple units crucial for encoding higher-level abstractions. Lastly, it is better suited for static, pre-trained models and may struggle to offer insights for fine-tuned or dynamically evolving architectures. Concept whitening [65] is another well-known technique. It works by modifying the latent space of neural networks to improve interpretability and disentanglement of learned representations. It introduces a specialised transformation layer into the network, which ensures that specific latent dimensions are decorrelated and aligned with human-understandable concepts. This is achieved through a whitening process that removes redundancy among features, making each dimension orthogonal to others while simultaneously associating them with predefined semantic concepts. By explicitly controlling the latent dimensions to represent interpretable concepts, concept whitening facilitates understanding and debugging of model behaviour and can potentially lead to better generalisation by reducing overfitting to irrelevant correlations in the data. Concept activation vectors (CAVs), presented by [PERSON] et al., represent one of the key methodologies in the concept-based explanations realm [66]. By defining a set of high-level concepts, such as \"striped\" or \"spotted\" for, say, animal classification, CAVs measure the alignment of these concepts with the model's internal representations and allow researchers to examine how a deep learning model responds to these human-interpretable concepts. This is achieved by calculating directional derivatives of model predictions with respect to the predefined concepts at an intermediate layer of the examined model. This enables the generation of more meaningful explanations in the form of human-understandable concepts compared to the CNNs' learnt high-level features or highlighted coarse-grained object locations. CAVs have been particularly valuable in medical imaging where model domain-specific explanations are crucial [97; 98]. [PERSON] et al.