Abstract: Explainable Artificial Intelligence (XAI) encompasses a suite of methodologies and processes designed to make the decision-making mechanisms of AI systems transparent and comprehensible to human users. The aim is to foster trust and confidence in AI outputs by elucidating how machine learning models arrive at their predictions. This transparency is critical for ensuring accountability, detecting and mitigating biases, and enhancing the overall fairness of AI systems. XAI methods provide clear, interpretable insights into the model's behavior, which is particularly important in sectors like healthcare, finance, and law where AI-driven decisions can have significant impacts on individuals. By enabling stakeholders, including data scientists, developers, domain experts, and business managers, to understand the rationale behind AI predictions, XAI supports informed decision-making and facilitates compliance with regulatory requirements. The integration of XAI into AI workflows promotes ethical AI development and deployment, ensuring that AI technologies are not only effective but also transparent and trustworthy. This paper delves into the importance of XAI in modern AI governance, exploring its role in overcoming barriers to AI adoption and ensuring responsible AI usage. Through detailed case studies and methodological discussions, we highlight how XAI can transform the landscape of AI applications by enhancing interpretability, fostering stakeholder trust, and ensuring regulatory compliance. The paper also addresses the challenges associated with implementing XAI, such as balancing model interpretability with performance and the complexity of interpreting deep learning models. Ultimately, XAI emerges as a crucial component in the pursuit of ethical and accountable AI, paving the way for more robust and equitable AI systems that can be confidently integrated into various critical domains.
Keywords: Transparency, Accountability, Bias Mitigation, Model Interpretability
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DOI:
10.17148/IJARCCE.2024.13622
