This volume constitutes the papers of several workshops which were held in conjunction with the 6th International Workshop on Explainable and Transparent AI and Multi-Agent Systems, EXTRAAMAS 2024, in Auckland, New Zealand, during May 6-10, 2024. The 13 full papers presented in this book were carefully reviewed and selected from 25 submissions. The papers are organized in the following topical sections: User-centric XAI; XAI and Reinforcement Learning; Neuro-symbolic AI and Explainable Machine Learning; and XAI & Ethics.
This volume constitutes the papers of several workshops which were held in conjunction with the 6th International Workshop on Explainable and Transparent AI and Multi-Agent Systems, EXTRAAMAS 2024, in Auckland, New Zealand, during May 6-10, 2024.
The 13 full papers presented in this book were carefully reviewed and selected from 25 submissions. The papers are organized in the following topical sections: User-centric XAI; XAI and Reinforcement Learning; Neuro-symbolic AI and Explainable Machine Learning; and XAI & Ethics.
.- User-centric XAI. .- Effect of Agent Explanations Using Warm and Cold Language on User Adoption of Recommendations for Bandit Problem. .- Evaluation of the User-centric Explanation Strategies for Interactive Recommenders. .- Can Interpretability Layouts Influence Human Perception of Offensive Sentences?. .- A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study. .- XAI and Reinforcement Learning. .- Learning Temporal Task Specifications From Demonstrations. .- Temporal Explanations for Deep Reinforcement Learning Agents. .- An Adaptive Interpretable Safe-RL Approach for Addressing Smart Grid Supply-side Uncertainties. .- Model-Agnostic Policy Explanations: Biased Sampling for Surrogate Models. .- Neuro-symbolic AI and Explainable Machine Learning. .- Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models. .- py ciu image: a Python library for Explaining Image Classification with Contextual Importance and Utility. .- Towards interactive and social explainable artificial intelligence for digital history. .- XAI & Ethics. .- Explainability and Transparency in Practice: A Comparison Between Corporate and National AI Ethics Guidelines in Germany and China. .- The Wildcard XAI: from a Necessity, to a Resource, to a Dangerous Decoy.
.- User-centric XAI. .- Effect of Agent Explanations Using Warm and Cold Language on User Adoption of Recommendations for Bandit Problem. .- Evaluation of the User-centric Explanation Strategies for Interactive Recommenders. .- Can Interpretability Layouts Influence Human Perception of Offensive Sentences?. .- A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study. .- XAI and Reinforcement Learning. .- Learning Temporal Task Specifications From Demonstrations. .- Temporal Explanations for Deep Reinforcement Learning Agents. .- An Adaptive Interpretable Safe-RL Approach for Addressing Smart Grid Supply-side Uncertainties. .- Model-Agnostic Policy Explanations: Biased Sampling for Surrogate Models. .- Neuro-symbolic AI and Explainable Machine Learning. .- Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models. .- py ciu image: a Python library for Explaining Image Classification with Contextual Importance and Utility. .- Towards interactive and social explainable artificial intelligence for digital history. .- XAI & Ethics. .- Explainability and Transparency in Practice: A Comparison Between Corporate and National AI Ethics Guidelines in Germany and China. .- The Wildcard XAI: from a Necessity, to a Resource, to a Dangerous Decoy.
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