Explainable Artificial Intelligence

.- Explainable AI in healthcare and computational neuroscience.

.- SRFAMap: a method for mapping integrated gradients of a CNN trained with statistical radiomic features to medical image saliency maps.

.- Transparently Predicting Therapy Compliance of Young Adults Following Ischemic Stroke.

.- Precision medicine in student health: Insights from Tsetlin Machines into chronic pain and psychological distress.

.- Evaluating Local Explainable AI Techniques for the Classification of Chest X-ray Images.

.- Feature importance to explain multimodal prediction models. A clinical use case.

.- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.

.- Increasing Explainability in Time Series Classification by Functional Decomposition.

.- Towards Evaluation of Explainable Artificial Intelligence in Streaming Data.

.- Quantitative Evaluation of xAI Methods for Multivariate Time Series - A Case Study for a CNN-based MI Detection Model.

.- Explainable AI for improved human-computer interaction and Software Engineering for explainability.

.- Influenciae: A library for tracing the influence back to the data-points.

.- Explainability Engineering Challenges: Connecting Explainability Levels to Run-time Explainability.

.- On the Explainability of Financial Robo-advice Systems.

.- Can I trust my anomaly detection system? A case study based on explainable AI..

.- Explanations considered harmful: The Impact of misleading Explanations on Accuracy in hybrid human-AI decision making.

.- Human emotions in AI explanations.

.- Study on the Helpfulness of Explainable Artificial Intelligence.

.- Applications of explainable artificial intelligence.

.- Pricing Risk: An XAI Analysis of Irish Car Insurance Premiums.

.- Exploring the Role of Explainable AI in the Development and Qualification of Aircraft Quality Assurance Processes: A Case Study.

.- Explainable Artificial Intelligence applied to Predictive Maintenance: Comparison of Post-hoc Explainability Techniques.

.- A comparative analysis of SHAP, LIME, ANCHORS, and DICE for interpreting a dense neural network in Credit Card Fraud Detection.

.- Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision data.

.- Ensuring Safe Social Navigation via Explainable Probabilistic and Conformal Safety Regions.

.- Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments.

.- AcME-AD: Accelerated Model Explanations for Anomaly Detection.

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• Hardware de rede
• Linguagem natural e tradução mecânica
• Inteligência artificial
• Tecnologia da informação: aspectos gerais

artificial intelligence;interpretable machine learning;causal inference & explanations;argumentative-based approaches for explanations;decomposition of neural network-based models for XAI;convolutional Neural Networks;natural language processing for explanations;reinforcement learning for enhancing XAI;explainability;neural networks;explainable Artificial Intelligence;Ante-hoc approaches for interpretability;Auto-encoders & explainability of latent spaces;Case-based explanations for AI systems;Graph neural networks for explainability;Interpreting & explaining Convolutional Neural Networks;Interpretable representational learning;Model-specific vs model-agnostic methods for XAI;Human rights for explanations in AI systems;Neuro-symbolic reasoning for XAI