DECODING THE MACHINE MIND: ENHANCING TRANSPARENCY IN AI MODELS
DOI:
https://doi.org/10.59367/s5vbqy58Keywords:
AI, XAI, AI-driven, AnalysisAbstract
As artificial intelligence (AI) and machine learning models continue to permeate various aspects of our lives, the inherent complexity and opaqueness of these models raise important concerns about their trustworthiness and accountability. This research paper delves into the concept of explainable AI (XAI) to improve transparency in AI models. We explore the challenges posed by "black-box" AI systems and present a comprehensive analysis of the strategies and techniques used to decipher their decision-making processes. Through case studies and real-world applications, we highlight the significance of achieving transparency in AI, not only for the sake of understanding the AI systems but also for addressing ethical and societal concerns. This paper offers an overview of emerging trends in the field of explainable AI and underscores the imperative of making AI models more interpretable, thereby paving the way for a more informed and accountable AI-driven world.
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