mlsanityData to 14 Oct 2026

Trustworthy XAI and Application

arXiv 2410.17139 ↗First posted 22 Oct 2024cs.AIcited by 0

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Abstract (arXiv)

Artificial Intelligence (AI) is an important part of our everyday lives. We use it in self-driving cars and smartphone assistants. People often call it a "black box" because its complex systems, especially deep neural networks, are hard to understand. This complexity raises concerns about accountability, bias, and fairness, even though AI can be quite accurate. Explainable Artificial Intelligence (XAI) is important for building trust. It helps ensure that AI systems work reliably and ethically. This article looks at XAI and its three main parts: transparency, explainability, and trustworthiness. We will discuss why these components matter in real-life situations. We will also review recent studies that show how XAI is used in different fields. Ultimately, gaining trust in AI systems is crucial for their successful use in society.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 71 references that are in the map. First posted on arXiv 22 Oct 2024.

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8 of 71 references.

References · 8

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  2. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  3. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  4. 2021Explainable AI: current status and future directionscited by 33
  5. 2023Active Learning on Medical Imagecited by 10
  6. 2020Trustworthy AI in the Age of Pervasive Computing and Big Datacited by 9
  7. 2023A Framework for Interpretability in Machine Learning for Medical Imagingcited by 9
  8. 2023Generative Adversarial Networks for Data Augmentationcited by 7

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