Abstract--Graph Transformers (GTs) have emerged as powerful architectures for graph-structured data, yet remain constrained by rigid designs and lack quantifiable interpretability methods. Current state-of-the-art GTs commit to fixed GNN types across all layers, missing potential benefits of depth-specific component selection, while their increasingly complex architectures become opaque black boxes where performance gains cannot be distinguished between meaningful structural patterns and spurious correlations. We redesign the GT attention mechanism through asymmetry, decoupling structural encoding from feature representation. Queries derive directly from node features, while keys and values come from graph neural network (GNN) transformations, separating how the model learns features from how it encodes graph structure. Within this asymmetric framework, we use Differentiable ARchiT ecture Search (DARTS) to select optimal GNN operators at each layer, enabling depth-wise heterogeneity inside the transformer attention itself, hence the name DARTS-GT . T o understand these discovered architectures, we develop the first quantitative interpretability framework for GTs through causal ablation that identifies which heads and nodes actually drive predictions. Our metrics: Head-deviation, Specialization, and Focus, reveal the specific components responsible for each prediction while enabling broader model comparison. Experiments across eight benchmarks demonstrate that DARTS-GT achieves state-of-the-art performance on four datasets while remaining competitive on others, with discovered architectures revealing dataset-specific patterns ranging from highly specialized to balanced GNN distributions. Our inter-pretability analysis reveals that visual attention salience and causal importance do not necessarily correlate, indicating that widely used visualization approaches may miss the components that actually matter for predictions. Crucially, the heterogeneous architectures found by DARTS-GT consistently produced more interpretable models than baseline GTs, establishing that Graph Transformers do not need to choose between performance and interpretability. For graph-structured data, Graph Transformers (GTs) have become a dominant architectural choice, combining attention mechanisms with graph-awareness [1], [2]. Their success spans protein structure-to-function prediction [3], drug discovery [4], and materials design [5], where understanding complex structural patterns is crucial. Current state-of-the-art GTs incorporate graph structure through GNN-Transformer combinations [2], [6], specialized positional encodings [7], and attention augmentation with structural biases [8].

Large Language Models (LLMs) are increasingly being used in education, yet their correctness alone does not capture the quality, reliability, or pedagogical validity of their problem-solving behavior, especially in mathematics, where multistep logic, symbolic reasoning, and conceptual clarity are critical. Conventional evaluation methods largely focus on final answer accuracy and overlook the reasoning process. To address this gap, we introduce a novel interpretability framework for analyzing LLM-generated solutions using undergraduate calculus problems as a representative domain. Our approach combines reasoning flow extraction and decomposing solutions into semantically labeled operations and concepts with prompt ablation analysis to assess input salience and output stability. Using structured metrics such as reasoning complexity, phrase sensitivity, and robustness, we evaluated the model behavior on real Calculus I to III university exams. Our findings revealed that LLMs often produce syntactically fluent yet conceptually flawed solutions, with reasoning patterns sensitive to prompt phrasing and input variation. This framework enables fine-grained diagnosis of reasoning failures, supports curriculum alignment, and informs the design of interpretable AI-assisted feedback tools. This is the first study to offer a structured, quantitative, and pedagogically grounded framework for interpreting LLM reasoning in mathematics education, laying the foundation for the transparent and responsible deployment of AI in STEM learning environments.

Contemporary machine learning (ML) techniques surpass traditional statistical methods in terms of their higher predictive power and their capability of processing a larger number of attributes. However, these novel ML algorithms generate models that have a complex structure which makes it difficult for their outputs to be interpreted with high precision. Another important issue is that a highly accurate predictive model might lack fairness by generating outputs that may result in discriminatory outcomes for protected subgroups. Thus, it is imperative to design predictive systems that are not only accurate but also achieve the desired fairness level. When used in certain contexts, predictive models, and strategies that rely on such models, are subject to laws and regulations that ensure fairness.

The objective of this article is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a distribution. In our work, we measure the model bias across sub-population distributions in the model output using the Wasserstein metric. To properly quantify the contributions of predictors, we take into account the favorability of both the model and predictors with respect to the non-protected class. The quantification is accomplished by the use of transport theory, which gives rise to the decomposition of the model bias and bias explanations to positive and negative contributions. To gain more insight into the role of favorability and allow for additivity of bias explanations, we adapt techniques from cooperative game theory.

If you like this article, please share it. Suggestions or feedbacks are always welcomed.

Most recent work on interpretability of complex machine learning models has focused on estimating $\textit{a posteriori}$ explanations for previously trained models around specific predictions. $\textit{Self-explaining}$ models where interpretability plays a key role already during learning have received much less attention. We propose three desiderata for explanations in general -- explicitness, faithfulness, and stability -- and show that existing methods do not satisfy them. In response, we design self-explaining models in stages, progressively generalizing linear classifiers to complex yet architecturally explicit models. Faithfulness and stability are enforced via regularization specifically tailored to such models. Experimental results across various benchmark datasets show that our framework offers a promising direction for reconciling model complexity and interpretability.

With the emergence of connected devices (e.g., smartphones and smartmeters), pervasive systems generate growing amounts of digital traces as users undergo their everyday activities. These traces are crucial to service providers to understand their customers, to increase the degree of personalization, and enhance the quality of their services. For instance, personal digital traces stemming from public transit smartcards help transportation providers understand the commuting patterns of users; the usage statistics of home appliances can be used to improve energy efficiency; on-street cameras provide police officers with new ways of investigating crimes; content generated through mobile and wearables (such as posts in online social media or GPS running routes in specialized websites such as those for fitness) can be used to provide tailored content to individuals; bank transaction logs can be used to spot unusual activity in accounts. However, sharing these digital traces generated by pervasive systems with service providers might raise concerns with regards to privacy. Indeed, the processing and analysis of these digital traces can surface latent information about the behavior of the users. While service providers have to store the usergenerated data in large databases that guarantee a certain level of privacy (e.g., from storing the traces in an anonymized manner using randomly-generated identifiers instead of the real user's name and surname to using more sophisticated privacy-preserving techniques such as differential privacy), third parties such as advertisers that have access to the traces can leverage machine learning techniques to reveal personal information about the users and expose their privacy [1]. This includes inferring personal information about users and identifying a single individual from a collection of user-generated traces. Moreover, these traces might reveal information about the significant places routinely visited by the user, enabling the service provider to infer a wide range of personal information, including the user's place of residence and work and their future locations. To a further extent, presence traces can also be used to identify a specific individual in a population.