Titlebook: Explainable Artificial Intelligence; First World Conferen Luca Longo Conference proceedings 2023 The Editor(s) (if applicable) and The Auth

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Closing the?Loop: Testing ChatGPT to?Generate Model Explanations to?Improve Human Labelling of?Spons Unfair Commercial Practices Directive (UCPD) in the European Union, or Section?5 of the Federal Trade Commission Act. Yet enforcing these obligations has proven to be highly problematic due to the sheer scale of the influencer market. The task of automatically detecting sponsored content aims to en

虛弱的神經(jīng)

Human-Computer Interaction and?Explainability: Intersection and?Terminologychnological artifacts or systems. Explainable AI (xAI) is involved in HCI to have humans better understand computers or AI systems which fosters, as a consequence, better interaction. The term “explainability” is sometimes used interchangeably with other closely related terms such as interpretabilit

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Explaining Deep Reinforcement Learning-Based Methods for?Control of?Building HVAC Systems learning techniques. However, due to the black-box nature of these algorithms, the resulting control policies can be difficult to understand from a human perspective. This limitation is particularly relevant in real-world scenarios, where an understanding of the controller is required for reliabili

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Necessary and?Sufficient Explanations of?Multi-Criteria Decision Aiding Models, with?and?Without Intclassify an instance in an ordered list of categories, on the basis of multiple and conflicting criteria. Several models can be used to achieve such goals ranging from the simplest one assuming independence among criteria - namely the weighted sum model - to complex models able to represent complex

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XInsight: Revealing Model Insights for?GNNs with?Flow-Based Explanationssystems. While this progress is significant, many networks are ‘black boxes’ with little understanding of the ‘what’ exactly the network is learning. Many high-stakes applications, such as drug discovery, require human-intelligible explanations from the models so that users can recognize errors and

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What Will Make Misinformation Spread: An XAI Perspective when making the decisions. Online social networks have a problem with misinformation which is known to have negative effects. In this paper, we propose to utilize XAI techniques to study what factors lead to misinformation spreading by explaining a trained graph neural network that predicts misinfo

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MORPH

Handbook of Practical Astronomyan aggregation of the explanations provided by the clients participating in the cooperation. We empirically test our proposal on two different tabular datasets, and we observe interesting and encouraging preliminary results.