ExplainabilityTransparencyTrustworthinesscs.AI
Trustworthy XAI and Application
MD Abdullah Al Nasim, A. S. M Anas Ferdous, Abdur Rashid, Fatema Tuj Johura Soshi, Parag Biswas, Angona Biswas, Kishor Datta Gupta
October 22, 2024
5 citations
Key Takeaways
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XAI addresses the "black box" problem: Deep neural networks and complex AI systems are difficult to understand, which creates concerns about accountability, bias, and fairness—even when the systems perform accurately. Explainable AI (XAI) helps make these systems more transparent and understandable to users and stakeholders.
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Three pillars support trustworthy AI: The paper identifies transparency, explainability, and trustworthiness as the three main components necessary for building trust in AI systems, suggesting that responsible AI requires attention to all three dimensions rather than just accuracy alone.
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Trust is essential for real-world AI adoption: The paper emphasizes that "gaining trust in AI systems is crucial for their successful use in society," indicating that without explainability and trustworthiness measures, AI systems may face resistance or fail to achieve their intended benefits in practical applications.
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XAI has broad applications across industries: The paper reviews recent studies showing XAI is being applied across different fields, suggesting that explainability is becoming a practical necessity rather than an optional feature as AI systems are deployed in high-stakes domains like self-driving cars and other critical applications.
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.