Research Papers

Explainable AI (XAI) research has traditionally focused on rational users, aiming to improve understanding and reduce cognitive biases. However, emotional factors play a critical role in how explanations are perceived and processed. Prior work shows that prior and task-generated emotions can negatively impact the understanding of explanation. Building on these insights, we propose a three-stage model for emotion-sensitive explanation grounding: (1) emotional or epistemic arousal, (2) understanding, and (3) agreement. This model provides a conceptual basis for developing XAI systems that dynamically adapt explanation strategies to users emotional states, ultimately supporting more effective and user-centered decision-making.

Machine Learning as a Service (MLaaS) has gained important attraction as a means for deploying powerful predictive models, offering ease of use that enables organizations to leverage advanced analytics without substantial investments in specialized infrastructure or expertise. However, MLaaS platforms must be safeguarded against security and privacy attacks, such as model extraction (MEA) attacks. The increasing integration of explainable AI (XAI) within MLaaS has introduced an additional privacy challenge, as attackers can exploit model explanations particularly counterfactual explanations (CFs) to facilitate MEA. In this paper, we investigate the trade offs among model performance, privacy, and explainability when employing Differential Privacy (DP), a promising technique for mitigating CF facilitated MEA. We evaluate two distinct DP strategies: implemented during the classification model training and at the explainer during CF generation.

Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization

Alexander Hinterleitner, Thomas Bartz-Beielstein

Despite the growing interest in Explainable Artificial Intelligence (XAI), explainability is rarely considered during hyperparameter tuning or neural architecture optimization, where the focus remains primarily on minimizing predictive loss. In this work, we introduce the novel concept of XAI consistency, defined as the agreement among different feature attribution methods, and propose new metrics to quantify it. For the first time, we integrate XAI consistency directly into the hyperparameter tuning objective, creating a multi-objective optimization framework that balances predictive performance with explanation robustness. Implemented within the Sequential Parameter Optimization Toolbox (SPOT), our approach uses both weighted aggregation and desirability-based strategies to guide model selection. Through our proposed framework and supporting tools, we explore the impact of incorporating XAI consistency into the optimization process. This enables us to characterize distinct regions in the architecture configuration space: one region with poor performance and comparatively low interpretability, another with strong predictive performance but weak interpretability due to low \gls{xai} consistency, and a trade-off region that balances both objectives by offering high interpretability alongside competitive performance. Beyond introducing this novel approach, our research provides a foundation for future investigations into whether models from the trade-off zone-balancing performance loss and XAI consistency-exhibit greater robustness by avoiding overfitting to training performance, thereby leading to more reliable predictions on out-of-distribution data.

Accurate and privacy-preserving diagnosis of ophthalmic diseases remains a critical challenge in medical imaging, particularly given the limitations of existing deep learning models in handling data imbalance, data privacy concerns, spatial feature diversity, and clinical interpretability. This paper proposes a novel Data efficient Image Transformer (DeiT) based framework that integrates context aware multiscale patch embedding, Low-Rank Adaptation (LoRA), knowledge distillation, and federated learning to address these challenges in a unified manner. The proposed model effectively captures both local and global retinal features by leveraging multi scale patch representations with local and global attention mechanisms. LoRA integration enhances computational efficiency by reducing the number of trainable parameters, while federated learning ensures secure, decentralized training without compromising data privacy. A knowledge distillation strategy further improves generalization in data scarce settings. Comprehensive evaluations on two benchmark datasets OCTDL and the Eye Disease Image Dataset demonstrate that the proposed framework consistently outperforms both traditional CNNs and state of the art transformer architectures across key metrics including AUC, F1 score, and precision. Furthermore, Grad-CAM++ visualizations provide interpretable insights into model predictions, supporting clinical trust. This work establishes a strong foundation for scalable, secure, and explainable AI applications in ophthalmic diagnostics.

Integrated Gradients (IG) is a widely used attribution method in explainable AI, particularly in computer vision applications where reliable feature attribution is essential. A key limitation of IG is its sensitivity to the choice of baseline (reference) images. Multi-baseline extensions such as Expected Gradients (EG) assume uniform weighting over baselines, implicitly treating all baseline images as equally informative. In high-dimensional vision models, this assumption often leads to noisy or unstable explanations. This paper proposes Weighted Integrated Gradients (WG), a principled approach that evaluates and weights baselines to enhance attribution reliability. WG introduces an unsupervised criterion for baseline suitability, enabling adaptive selection and weighting of baselines on a per-input basis. The method preserves the core axiomatic properties of IG in a generalized weighted-baseline form. Under an expected, proxy-based fitness--relevance monotonicity assumption, WG provides a probabilistic justification for assigning larger weights to more informative baselines. Experiments on commonly used image datasets and models show that WG improves over EG under our protocol, with up to 36% gains across evaluated convolutional and Transformer architectures. These gains come with additional fitness-evaluation cost, so WG should be viewed as an attribution-fidelity trade-off rather than a faster alternative to EG. By moving beyond the assumption that all baselines contribute equally, Weighted Integrated Gradients offers a clearer and more reliable approach to explaining computer-vision models, improving both understanding and practical usability in explainable AI.

Quantitative Systems Pharmacology (QSP) promises to accelerate drug development, enable personalized medicine, and improve the predictability of clinical outcomes. Realizing this potential requires effectively managing the complexity of mathematical models representing biological systems. Here, we present and validate a novel QSP workflow--CRISP (Contextualized Reduction for Identifiability and Scientific Precision)--that addresses a central challenge in QSP: the problem of complexity and over-parameterization, in which models contain irrelevant parameters that obscure interpretation and hinder predictive reliability. The CRISP workflow begins with a literature-derived model, constructed to be comprehensive and unbiased by integrating prior mechanistic insights. At the core of the workflow is the Manifold Boundary Approximation Method (MBAM), a reduction technique that simplifies models while preserving mechanistic structure and predictive fidelity. By applying MBAM in a context-specific manner, CRISP links parsimonious models directly to predictions of interest, clarifying causal structure and enhancing interpretability. The resulting models are computationally efficient and well-suited to key QSP tasks, including virtual population generation, experimental design, toxicology, and target discovery. We demonstrate the utility of CRISP on case studies involving the coagulation cascade and SHIV infection, and identify promising directions for improving the efficacy of bNAb therapies for HIV. Together, these results establish CRISP as a general-purpose QSP workflow for turning complex mechanistic models into tools for precise scientific reasoning to guide pharmacological and regulatory decision-making.

Analyzing Cognitive Differences Among Large Language Models through the Lens of Social Worldview

Jiatao Li, Yanheng Li, Xiaojun Wan

Large Language Models significantly influence social interactions, decision-making, and information dissemination, underscoring the need to understand the implicit socio-cognitive attitudes, referred to as "worldviews", encoded within these systems. Unlike previous studies predominantly addressing demographic and ethical biases as fixed attributes, our study explores deeper cognitive orientations toward authority, equality, autonomy, and fate, emphasizing their adaptability in dynamic social contexts. We introduce the Social Worldview Taxonomy (SWT), an evaluation framework grounded in Cultural Theory, operationalizing four canonical worldviews, namely Hierarchy, Egalitarianism, Individualism, and Fatalism, into quantifiable sub-dimensions. Through extensive analysis of 28 diverse LLMs, we identify distinct cognitive profiles reflecting intrinsic model-specific socio-cognitive structures. Leveraging principles from Social Referencing Theory, our experiments demonstrate that explicit social cues systematically modulate these profiles, revealing robust patterns of cognitive adaptability. Our findings provide insights into the latent cognitive flexibility of LLMs and offer computational scientists practical pathways toward developing more transparent, interpretable, and socially responsible AI systems

Attribution algorithms are essential for enhancing the interpretability and trustworthiness of deep learning models by identifying key features driving model decisions. Existing frameworks, such as InterpretDL and OmniXAI, integrate multiple attribution methods but suffer from scalability limitations, high coupling, theoretical constraints, and lack of user-friendly implementations, hindering neural network transparency and interoperability. To address these challenges, we propose Attribution-Based Explainability (ABE), a unified framework that formalizes Fundamental Attribution Methods and integrates state-of-the-art attribution algorithms while ensuring compliance with attribution axioms. ABE enables researchers to develop novel attribution techniques and enhances interpretability through four customizable modules: Robustness, Interpretability, Validation, and Data & Model. This framework provides a scalable, extensible foundation for advancing attribution-based explainability and fostering transparent AI systems. Our code is available at: https://github.com/LMBTough/ABE-XAI.

Autonomous Vehicles (AVs) rely on artificial intelligence (AI) to accurately detect objects and interpret their surroundings. However, even when trained using millions of miles of real-world data, AVs are often unable to detect rare failure modes (RFMs). The problem of RFMs is commonly referred to as the "long-tail challenge", due to the distribution of data including many instances that are very rarely seen. In this paper, we present a novel approach that utilizes advanced generative and explainable AI techniques to aid in understanding RFMs. Our methods can be used to enhance the robustness and reliability of AVs when combined with both downstream model training and testing. We extract segmentation masks for objects of interest (e.g., cars) and invert them to create environmental masks. These masks, combined with carefully crafted text prompts, are fed into a custom diffusion model. We leverage the Stable Diffusion inpainting model guided by adversarial noise optimization to generate images containing diverse environments designed to evade object detection models and expose vulnerabilities in AI systems. Finally, we produce natural language descriptions of the generated RFMs that can guide developers and policymakers to improve the safety and reliability of AV systems.

Explaining unsolvability of planning problems is of significant research interest in Explainable AI Planning. AI planning literature has reported several research efforts on generating explanations of solutions to planning problems. However, explaining the unsolvability of planning problems remains a largely open and understudied problem. A widely practiced approach to plan generation and automated problem solving, in general, is to decompose tasks into sub-problems that help progressively converge towards the goal. In this paper, we propose to adopt the same philosophy of sub-problem identification as a mechanism for analyzing and explaining unsolvability of planning problems in hybrid systems. In particular, for a given unsolvable planning problem, we propose to identify common waypoints, which are universal obstacles to plan existence; in other words, they appear on every plan from the source to the planning goal. This work envisions such waypoints as sub-problems of the planning problem and the unreachability of any of these waypoints as an explanation for the unsolvability of the original planning problem. We propose a novel method of waypoint identification by casting the problem as an instance of the longest common subsequence problem, a widely popular problem in computer science, typically considered as an illustrative example for the dynamic programming paradigm. Once the waypoints are identified, we perform symbolic reachability analysis on them to identify the earliest unreachable waypoint and report it as the explanation of unsolvability. We present experimental results on unsolvable planning problems in hybrid domains.

Background and aim: Considering the scope of the application of artificial intelligence beyond the field of computer science, one of the concerns of researchers is to provide quality explanations about the functioning of algorithms based on artificial intelligence and the data extracted from it. The purpose of the present study is to validate the Italian version of system causability scale (I-SCS) to measure the quality of explanations provided in a xAI. Method: For this purpose, the English version, initially provided in 2020 in coordination with the main developer, was utilized. The forward-backward translation method was applied to ensure accuracy. Finally, these nine steps were completed by calculating the content validity index/ratio and conducting cognitive interviews with representative end users. Results: The original version of the questionnaire consisted of 10 questions. However, based on the obtained indexes (CVR below 0.49), one question (Question 8) was entirely removed. After completing the aforementioned steps, the Italian version contained 9 questions. The representative sample of Italian end users fully comprehended the meaning and content of the questions in the Italian version. Conclusion: The Italian version obtained in this study can be used in future research studies as well as in the field by xAI developers. This tool can be used to measure the quality of explanations provided for an xAI system in Italian culture.

We propose a novel symbolic modeling framework for decision-making under risk that merges interpretability with the core insights of Prospect Theory. Our approach replaces opaque utility curves and probability weighting functions with transparent, effect-size-guided features. We mathematically formalize the method, demonstrate its ability to replicate well-known framing and loss-aversion phenomena, and provide an end-to-end empirical validation on synthetic datasets. The resulting model achieves competitive predictive performance while yielding clear coefficients mapped onto psychological constructs, making it suitable for applications ranging from AI safety to economic policy analysis.