# Explainable Artificial Intelligence: Advancements and Limitations
## 5 Conceptual Explanations
[99] proposed concept activation regions (CARs) which extend the CAV approach to address its limitations in modelling scattered concept examples in a DNN's latent space. Unlike CAVs, which assume that concept examples align with a single direction in the latent space, CARs represent concepts as regions encompassing multiple clusters. This representation uses the kernel trick and support vector classifiers to define CARs. CARs enable describing how concepts relate to DNN predictions and also show how specific features correspond to concepts. Additionally, CARs demonstrate the potential for DNNs to autonomously identify established scientific concepts such as grading systems in prostate cancer analysis. Presented as an extension to the earlier idea [100; 101] of first predicting the concepts and then using the predicted concepts to predict a final target, concept bottleneck models (CBMs) [67] offer a compelling approach within the realm of conceptual explanations in XAI. These models were designed to provide interpretable decisions by incorporating human-understandable concepts into their decision-making process. In a CBM, the model first predicts a set of predefined concepts--such as \"texture\", \"shape\", or \"tumour size\"--which serve as high-level, interpretable features. These predicted concepts are then used as inputs to make the final decision. By doing so, CBMs allow users to directly inspect the model's reasoning at an intermediate level, offering transparency into how each concept contributes to the outcome. This structured approach not only makes the decision-making process more understandable but also allows for interventions at the concept level, enabling users to correct mispredicted concepts before they affect the final output. In this direction, there have been several works exploring efficient ways to achieve systematic interventions and model corrections [102; 103; 104; 105]. CBMs differ from CAVs in how they treat concepts. While CBMs are trained with the explicit goal of predicting and utilising predefined concepts as an integral part of their decision pipeline, CAVs operate in a post hoc manner. CAVs are used to probe and analyse a trained model's internal representations to measure how well these representations align with specific concepts, without requiring the model to explicitly predict those concepts during training. In other words, CAVs extract and quantify the influence of concepts after the model has been trained, while CBMs actively incorporate and rely on concepts throughout the learning process. This distinction gives CBMs a unique advantage when it comes to interpretability and control, as they offer built-in transparency and allow for direct correction at the concept level, enhancing both the interpretability and robustness of the model. Several extensions were introduced to improve CBMs [106; 107; 108; 109; 110]. One key drawback of CBMs is their high cost as they require manual concept annotation for every training image. This process can be time-consuming and resource-intensive, especially when working with large datasets. One solution is proposed in [75] where class-wise attributes are used to train CBMs which significantly reduced the annotation cost. Another solution is to integrate CAVs [66] in CBMs, which requires only a set of positive and negative examples per concept, making them less annotation-intensive compared to the full concept annotations required by CBMs. Moreover, CAVs can even be derived from a completely different dataset to form _concept banks_ which then can be employed to explain models trained with related datasets, making CAVs more flexible and scalable. In this context, post hoc CBMs [69] achieve this integration to create a more efficient and controlled approach. They work by leveraging CAVs to obtain concept values, eliminating the need for intermediate concept predictions. Following that, a single layer is introduced as a bottleneck inspired by CBMs to map the concept values to the final classes.