World Conference on eXplainable Artificial Intelligence

🔎 𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ Intrinsically interpretable explainable AI ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Andrea Passerini Christin Seifert Bartosz Zieliński Dawid Rymarczyk, PhD Khawla Elhadri 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dVdJSyC3 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: Intrinsically interpretable (deep learning) methods aim to bridge the gap between accuracy and interpretability. Their idea is to combine deep representation learning with easily understandable decision layers to construct a model with a traceable reasoning process. Prominent examples are ProtoPNet, Concept Bottleneck Models, B-Cos, and their derivatives. These decision models are designed to transparently reveal the logic behind their predictions during inference. Despite their advantages, intrinsically interpretable models require unique architectures and training procedures, and they can show a drop in performance compared to their black-box counterparts. Furthermore, learning prototypes that mimic human reasoning is still an open challenge. This track aims to explore the latest challenges and developments in intrinsically interpretable models, including evaluation techniques, the interpretability-accuracy trade-off, theoretical foundations, their practical applications, and their broader impact on society. #interpretability #intrinsic #explainability #transparency #deeplearning #artificialintelligence

🔎 𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ Generative AI meets explainable AI ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Przemyslaw Biecek Christin Seifert Sebastian Lapuschkin Sonia Laguna Cillero Chirag Agarwal Hubert Baniecki Bartłomiej Sobieski 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dFnd_4dp 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: Generative Artificial Intelligence (GenAI) is revolutionizing machine learning research and rapidly pushing the boundaries of computer vision, natural language processing, and multi-modal learning. This phenomenon has already raised significant concerns regarding the extreme complexity of machine learning systems. The effective and safe implementation of GenAI solutions must align with a deeper understanding of their decision-making processes. Historically focused on purely predictive models, the eXplainable Artificial Intelligence (XAI) domain has tackled the challenge of understandability for years. However, current XAI methods often constrain human creativity when debugging machine learning systems. The field of XAI is now at a pivotal point where the focus is shifting from simply understanding AI model outputs and inner logic to a new paradigm. In this paradigm, explainability becomes a tool for verifying, mining, and exploring information, including outputs from AI and other automated decision-making systems. This special track emphasizes the critical role generative AI can play in enhancing explainability, enabling constructive verification of both AI model outputs and human decisions/intuitions. With this in mind, we distinguish between two key themes: i)How GenAI can advance the frontier of XAI (GenAI for XAI). ii) How the XAI experience can address critical challenges in GenAI (XAI for GenAI). The goal of this track is to bridge the two domains and integrate their development, fostering innovation and collaboration. #general #artificialintelligence #deeplearning #interpretability #generativeAI #explainability #enhancement #innovation #explanations #GenXAI

𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ Explainable AI for Relational Learning ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: FRANCESCO GIANNINI @michelangelo diligenti 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dCq4mYpg 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: In the world of machine learning, where entities are often seen as independent, relational learning stands out by recognizing the crucial connections between them. This field, equipped with powerful tools like Graph Neural Networks and Knowledge Graph Embeddings, offers a way to understand complex, interconnected data. However, a significant limitation persists: the “black box” nature of these methods, which obscures their underlying decision-making processes. This is where explainable and interpretable methods become essential. By shedding light on how these models work, we can gain valuable insights into the relationships within the data. Moreover, the inherent graphical structure of relational data provides a unique opportunity to develop eXplainable AI (XAI) methods that leverage this structure for interpretation. Despite its potential, this avenue remains largely unexplored in current XAI approaches. Bridging this gap is crucial. Developing interpretable-by-design models and effective XAI methodologies specifically for relational data and methods will not only enhance trust and understanding, but also unlock the full potential of relational learning across various domains. This involves establishing clear theoretical foundations and definitions for XAI in the context of relational learning, paving the way for more transparent and insightful analyses. #relational #learning #explainability #artificialintelligence #graphneuralnetworks #knowledgegraph #blackboxes #machinelearning #relationaldata

𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ Concept-based explainable AI ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Alberto Termine Arianna Casanova Flores Eleonora Poeta Mateo Espinosa Zarlenga Pietro Barbiero 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dVY_5Hjs 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: Existing explainable AI techniques, such as LIME or SHAP, primarily produce feature-level explanations, i.e., they focus on identifying the features (or sets of features) of the input that are most responsible for a given outcome. These techniques are effective with machine learning (ML) models that use humanly interpretable features such as ‘income’ or ‘age’. However, they are much less effective with more contemporary deep learning (DL) models, which typically rely on low-level features, such as the pixels of an image, that lack human-interpretable meaning. Overcoming this issue requires a shift from feature-based to concept-based explanations, i.e., explanations involving higher-level variables (called concepts) that human users can easily understand and possibly manipulate. In recent years, a variety of concept-based XAI techniques have been proposed, including both inherently interpretable models and post-hoc explainability methods. These techniques tend to provide more effective explanations, exhibit greater stability under perturbations, and offer enhanced robustness to adversarial attacks. Despite these benefits, research in concept-based XAI remains in its early stages, with opportunities for further advancement, particularly in the context of real-world applications. This special track seeks to engage the XAI community in advancing concept-based methodologies and promoting their application in domains where high-level, human-interpretable explanations can enhance AI-system user interaction. Submissions are invited that address novel methods for generating concept-based explanations, explore the application of both new and existing concept-based AI techniques across specific domains, and propose evaluation frameworks and metrics for assessing the efficacy of such explanations. #concept #interpretability #explainability #deeplearning #artificialintelligence #realworld #applications #highlevel #variables

🔎 𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ XAI and argumentation ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Giulia Vilone Lucas Rizzo 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/daGB8MjY 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: Argumentation in AI encompasses formal frameworks and computational models that study, replicate, and support reasoning processes involving constructing, evaluating, and comparing arguments. Rooted in logic, philosophy, and cognitive science, argumentation enables systems to engage in tasks like decision-making, negotiation, and explanation by presenting structured arguments and counterarguments. This capability plays an important role in enhancing Explainable AI (XAI), as it provides transparent, intuitive, and interpretable justifications for its decisions. Key applications include resolving conflicts in multi-agent systems, supporting human-computer interaction through transparent reasoning, and providing clear and intuitive justifications for AI decisions. Integrating XAI with argumentation represents a frontier in enhancing AI systems’ transparency, accountability, and user trust. Open research quests include the exploration of the synergies between XAI and argumentation theory, emphasising how argumentation frameworks can be leveraged to generate, structure, and present intuitive explanations in AI systems. This entails investigating the development of argumentation-based methods for interpretability, the role of argumentation in human-AI interaction, and the formalisation of explainability using argumentation models. Pivotal to the successful integration of argumentation and XAI are contributions addressing practical challenges, such as the scalability of argumentation-based explanations in large-scale AI models and the evaluation of these explanations in real-world applications. Lastly, encouraging interdisciplinary collaborations and research initiatives can help overcome these challenges and advance the integration of XAI with argumentation, fostering progress towards AI systems that are more understandable, ethical, and socially acceptable. #XAI #argumentation #explainability #reasoning #explanations #argumentationframeworks #negotiation #arguments #logic #artificialintelligence

🔎 𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ Actionable explainable AI ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: @gregoire montavon Lorenz Linhardt Caroline Petitjean Gian Antonio Susto 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/d5yfkzvm 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: Following the success of Explainable AI in generating faithful and understandable explanations of complex ML models, there has been increasing attention on how the outcomes of Explainable AI can be systematically used to enable meaningful actions. These considerations are studied within the subfield of Actionable XAI. In particular, research questions relevant to this subfield include (1) what types of explanations are most helpful in enabling human experts to achieve more efficient and accurate decision-making, (2) how one can systematically improve the robustness and generalization ability of ML models or align them with human decision making and norms based on human feedback on explanations, (3) how to enable meaningful actioning of real-world systems via interpretable ML-based digital twins, and (4) how to evaluate and improve the quality of actions derived from XAI in an objective and reproducible manner. This special track will address both the technical and practical aspects of Actionable XAI. This includes the question of how to build highly informative explanations that form the basis for actionability, aiming for solutions that are interoperable with existing explanation techniques such as Shapley values, LRP or counterfactuals, and existing ML models. This special track will also cover the exploration of real-world use cases where these actions lead to improved outcomes. #explainableAI #LRP #counterfactuals #shapley #models #deeplearning #actionability #interpretability #digitaltwins #decisionmaking

🔎 𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ Explainable AI in smart-mobility ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Gonzalez-Diaz Maurizio Mongelli 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dBsbndkw 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: The rapid evolution of intelligent systems in smart mobility presents unique challenges and opportunities for the research and application of explainable AI (XAI). This special track invites original contributions that explore the development, application, and evaluation of XAI methodologies tailored to the complexities of smart mobility. Topics of interest include explainable models for autonomous vehicles, interpretable decision-making in real-time traffic management, transparent algorithms for predictive maintenance in transportation systems, and beyond. Submissions are encouraged to address specific technical challenges, such as balancing model performance with transparency, ensuring accountability in safety-critical applications, and fostering trust among diverse stakeholders, from engineers to end users. Papers that propose novel frameworks, present case studies, or delve into the ethical and societal implications of explainability in smart mobility are particularly welcome. Contributions are not limited to these topics, as we aim to encourage a broad exploration of ideas and approaches relevant to the intersection of XAI and smart mobility. This track aspires to create a platform for innovative discussions, promoting advancements in XAI that can be effectively integrated into intelligent mobility solutions. Researchers and practitioners are invited to share their findings, address the unique challenges of this domain, and contribute to shaping the future of explainable AI in transportation systems. #smartmobility #artificialintelligence #interpretability #trustworthyAI #responsibleAI #safety #critical #applications #realtime #traffic #management #transportation

🔎 𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ Explainable AI in finance ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Paolo Giudici Paola Cerchiello 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dayzHKB7 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: The rapid expansion of Artificial Intelligence (AI) applications in finance necessitates the introduction of statistical methods that can assess their quality, not only from a technical viewpoint (accuracy, sustainability) but also from an ethical viewpoint (explainability, fairness). In this special track, we contribute to filling this gap by calling for papers that develop consistent statistical metrics to measure the sustainability, accuracy, fairness, and explainability of AI applications in finance. We also call for work that shows their practical applications and showcases software packages for their implementations. All areas of finance are considered, including credit lending, asset management and insurance. #fairness #machinelearning #artificialintelligence #statistical #metrics #finance #credintlending #assetmanagement #insurance #modeling #trustwrothyAI

🔎 𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ E xplainable and interactive hybrid decision-making ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Federico Giannini Chiara Natali Roberto Pellungrini Andrea Pugnana 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dpGq375C 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: This special track focuses on advancing human-AI interaction with explainable artificial intelligence (XAI) systems by integrating traditional XAI techniques with diverse external knowledge sources. Hybrid human-AI decision-making, central to this exploration, refers to a collaborative framework in which humans and AI systems jointly contribute to the analysis, evaluation, and resolution of decision-making tasks. This synergy leverages the complementary strengths of humans—such as domain expertise, intuition, ethical reasoning, and contextual understanding—with AI’s capabilities, including data processing, pattern recognition, and predictive modeling. In particular, the track emphasizes hybrid decision-making systems that deliver accurate predictions and provide contextually meaningful explanations tailored to diverse user needs. These systems combine structured knowledge bases, unstructured data, and multidisciplinary approaches to create a framework for explanation. Structured knowledge bases ensure consistency and reliability by offering formal, organized explanations. Unstructured knowledge, derived from text, images, and domain-specific insights, adds nuanced, adaptable explanations that address real-world complexities. A multidisciplinary perspective bridges these methods, focusing on user-centric design to ensure that explanations are accessible, transparent, and actionable for varied audiences, including experts, novices, and interdisciplinary stakeholders. By addressing challenges in data integration, context awareness, and explanation personalization, this track highlights how explainable hybrid systems can enhance decision quality, foster trust, and ensure ethical accountability. Submissions are encouraged to present innovative methodologies and studies that advance systems capable of explaining “how” decisions are made and ''why” they matter, enabling transparent and collaborative human-AI interaction. #artificialIntelligence #humanAI #interaction #decisionmaking #ethicalreasoning #contextualunderstanding #patternrecognition #modeling #explanations #usercentricdesign #transparency

🔎 𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ Explainability privacy and fairness in trustworthy ai ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Fatima Ezzeddine Omran Ayoub Martin Gjoreski Silvia Giordano Marc Langheinrich 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dRPB5ycS 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: Recent regulatory frameworks have emphasized the importance of responsibility in artificial intelligence (AI), with a focus on key desiderata such as fairness, explainability, and privacy. These principles are central to ensuring AI systems are trustworthy and align with societal values. However, theoretical and empirical studies have increasingly highlighted the inherent tensions between these desiderata, which often conflict with one another under certain conditions. For instance, research has demonstrated a two-way tension between privacy and explainability. On the one hand, providing explanations for model predictions can inadvertently expose sensitive information, leading to privacy risks such as adversarial privacy attacks. On the other hand, employing privacy-preserving techniques like differential privacy can degrade the quality and utility of explanations, making it challenging to interpret the model’s behavior. Similarly, there is a complex relationship between fairness and both privacy and explainability. Efforts to achieve fairness in machine learning models can sometimes introduce privacy vulnerabilities or undermine the interpretability of the system. For example, fairness-enhancing algorithms may require sensitive attribute information to ensure equitable outcomes, potentially increasing privacy risks. At the same time, ensuring fairness can complicate the generation of clear, intuitive explanations, as fairness adjustments may obscure the model’s decision-making processes. Research that explores these interdependencies and sheds light on the trade-offs between fairness, explainability, and privacy is crucial. Studies that examine how these desiderata interact both in theory and practice are particularly valuable for guiding the development of AI systems that balance these often-competing priorities effectively. #responsibleAI #explainability #theory #practice #regulatory #frameworks #tension #privacy #trustworthyAI