The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) …
Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as …
Machine learning is increasingly used to inform decision making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in …
Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning …
J Zhang, D Tao - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the Internet …
A number of algorithms in the field of artificial intelligence offer poorly interpretable decisions. To disclose the reasoning behind such algorithms, their output can be explained …
Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks. However, due to their over-parameterized black-box …
Despite the proliferation of explainable AI (XAI) methods, little is understood about end- users' explainability needs and behaviors around XAI explanations. To address this gap and …
Y Liu, G Li, L Lin - IEEE Transactions on Pattern Analysis and …, 2023 - ieeexplore.ieee.org
Existing visual question answering methods often suffer from cross-modal spurious correlations and oversimplified event-level reasoning processes that fail to capture event …