# Towards Explainable Pedestrian Behavior Prediction:
## 1 Introduction and Related Work
[10] highlights that Transformer-based architectures offer advantages in interpretability due to their attention mechanisms, which inherently reveal which parts of the input the model focuses on during prediction. Similarly, ref. [11] introduces a dynamic Bayesian network that models the influence of interactions and social signals. This approach combines visual information with probabilistic inference techniques to provide transparent explanations, particularly by quantifying the relative importance of individual characteristics in shaping the predicted likelihood of pedestrian actions. It is also worth noting that there is no universally accepted definition of interpretability and explainability within the machine learning community, and these terms are often used interchangeably [12]. However, some authors such as [13] emphasize the distinction between these concepts by framing them as questions: interpretability raises the question of _'How does the model work? '_ while explainability attempts to answer _'What additional insights can the model provide?'_[12]. Additionally, the term interpretability is often associated with the degree to which a human can comprehend the rationale behind a decision, while explanation refers to the response to a why question [14]. In this paper, we use both terms complementarily to explain why the model predicts specific pedestrian behavior. The explainability task, in the context of autonomous driving, has gained prominence due to the imperative need to clarify every decision made by the autonomous vehicles (AVs). This need arises from various disciplinary perspectives and opinions, centering on the issues and growing concerns caused by artificial intelligence (AI) systems, generating the necessity to establish regulations and standards that ensure control over their data and predictions made by the AV. The need for explainability is summarized in Figure 1,which highlights the primary concerns related to societal and regulatory demands, the expectations from explainable AI, and various perspectives on explainability [15]. On the other hand, it is crucial to acknowledge that the success of machine learning projects is significantly influenced by the quality and relevance of the datasets used for model, training, and testing. In fact, a well-chosen dataset has the potential to enhance the accuracy and efficiency of the model, whereas an inaccurate selection can yield unfavorable results. Furthermore, the characteristics of the datasets play a crucial role in shaping the behavior of a model. It is essential to consider that a model's performance in real-world scenarios may be compromised if its deployment context significantly differs from the training and evaluation datasets [16]. Moreover, it is important to highlight that a well-chosen dataset can play a key role in facilitating the extraction of explainable features from pedestrian behavior datasets. For instance, there are studies that aim to comprehensively document the creation and utilization of datasets through the development of datasheets. These datasheets provide valuable information regarding the motivation behind dataset creation, composition details, collection processes, preprocessing techniques, distribution methods, besides data usage and maintenance guidelines [16]. Similarly, research efforts are being dedicated to various aspects of datasets, such as tracking and controlling dataset versions [17] and exploring data provenance [18]. It is important to highlight that there are some data selection methods which focus on choosing the most informative training examples for machine learning tasks across a specific dataset [19]. Nevertheless, it is worth noting that the topic of dataset selection approaches is relatively under-discussed within the ML research community. Regarding the pedestrian behavior datasets, there are numerous datasets which include pedestrian annotations.