Towards Explainable AI: Enhancing Trust in Autonomous Systems
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Abstract
The increasing reliance on AI in critical applications necessitates the development of explainable AI (XAI) systems. This paper examines the methodologies for making AI systems interpretable without compromising performance. It categorizes existing XAI techniques, such as feature attribution and surrogate models, and evaluates their effectiveness in domains like healthcare, finance, and autonomous vehicles. The study highlights the role of XAI in fostering trust among users and regulators, advocating for standardized evaluation metrics and frameworks for implementation.
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References
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