Technology Category
Humans and AI: Explainable AI (XAI) for Human UnderstandingPhilosophy and Ethics of AI: Safety, Robustness & TrustworthinessComputer Vision: Interpretability, Explainability, and Transparency
Trustworthy XAI and Application
🤖 AI Summary
The “black-box” nature of deep neural networks and other AI systems has precipitated a crisis of trust. Method: This paper proposes a unified framework for trustworthy and explainable artificial intelligence (XAI), introducing, for the first time, a systematic three-dimensional core of trustworthiness—comprising transparency, explainability, and reliability—and establishing a cross-domain, transferable trust assessment paradigm. The framework integrates model-agnostic explanation techniques (e.g., SHAP, LIME), causal inference, fairness-aware optimization, and quantitative trust metrics. Contribution/Results: Empirical evaluation in high-stakes domains—including autonomous driving and intelligent assistants—demonstrates that the framework significantly improves user trust (+37%) and decision traceability. It supports ethical compliance and safe deployment of AI systems, providing a reusable methodological foundation to bridge the gap between XAI theory and real-world application.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Addressing AI black box complexity for accountability
Ensuring XAI transparency, explainability, and trustworthiness
Building trust in AI systems for societal adoption
Innovation
Methods, ideas, or system contributions that make the work stand out.
XAI focuses on transparency in AI
XAI ensures explainability of neural networks
XAI enhances trustworthiness in ethical AI
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