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Explainable interactive projections of images
Dimension reductions (DR) help people make sense of image collections by organizing images in the 2D space based on similarities. However, they...
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Explainable Artificial Intelligence for Combating Cyberbullying
Cyberbullying has become a serious societal issue that affects millions of people globally, particularly the younger generation. Although existing...
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Scientific Exploration and Explainable Artificial Intelligence
Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque....
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Explainable machine learning models with privacy
The importance of explainable machine learning models is increasing because users want to understand the reasons behind decisions in data-driven...
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Percentages and reasons: AI explainability and ultimate human responsibility within the medical field
With regard to current debates on the ethical implementation of AI, especially two demands are linked: the call for explainability and for ultimate...
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AI Guidelines and Ethical Readiness Inside SMEs: A Review and Recommendations
Small and medium enterprises (SMEs) represent a large segment of the global economy. As such, SMEs face many of the same ethical and regulatory...
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Understanding the influence of AI autonomy on AI explainability levels in human-AI teams using a mixed methods approach
An obstacle to effective teaming between humans and AI is the agent’s "black box" design. AI explanations have proven benefits, but few studies have...
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Interpretable representations in explainable AI: from theory to practice
Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and...
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Explainable AI for the Olive Oil Industry
Understanding Machine Learning results for the quality assessment of olive oil is hard for non-ML experts or olive oil producers. This paper...
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Attention guided grad-CAM : an improved explainable artificial intelligence model for infrared breast cancer detection
Explainable artificial intelligence (XAI) can help build trust between AI models and healthcare professionals in the context of medical image...
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Toward interpretable credit scoring: integrating explainable artificial intelligence with deep learning for credit card default prediction
In recent years, the increasing prevalence of credit card usage has raised concerns about accurately predicting and managing credit card defaults....
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Cybersecurity Data Science: Toward Advanced Analytics, Knowledge, and Rule Discovery for Explainable AI Modeling
In a computing context, cybersecurity technology and operations are constantly changing, and data science is driving the change. Building a...
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Practical early prediction of students’ performance using machine learning and eXplainable AI
Predicting students’ performance in advance could help assist the learning process; if “at-risk” students can be identified early on, educators can...
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Explainable empirical risk minimization
The successful application of machine learning (ML) methods increasingly depends on their interpretability or explainability. Designing explainable...
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Towards Autonomous Developmental Artificial Intelligence: Case Study for Explainable AI
State-of-the-art autonomous AI algorithms such as reinforcement learning and deep learning techniques suffer from high computational complexity, poor...
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Conceptualizing understanding in explainable artificial intelligence (XAI): an abilities-based approach
A central goal of research in explainable artificial intelligence (XAI) is to facilitate human understanding. However, understanding is an elusive...
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Statutory Professions in AI Governance and Their Consequences for Explainable AI
Intentional and accidental harms arising from the use of AI have impacted the health, safety and rights of individuals. While regulatory frameworks...
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CENTAURO: An Explainable AI Approach for Customer Loyalty Prediction in Retail Sector
Customer loyalty is a crucial factor for retail business success. This paper illustrates an AI approach, named CENTAURO, to learn customer loyalty...
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Towards a Taxonomy for Explainable AI in Computational Pathology
This chapter aims to provide a common understanding of some important aspects and factors involved in building a human-centred AI laboratory for...
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MTUNet + + : explainable few-shot medical image classification with generative adversarial network
Medical imaging, a cornerstone of disease diagnosis and treatment planning, faces the hurdles of subjective interpretation and reliance on...
