Advancement in Explainable AI: Bringing Transparency and Interpretability to Machine Learning Models for Use in High-Stakes Decisions

AI and ML have rapidly altered healthcare, finance, and criminal justice. These models are being used for high-stakes decision-making, raising questions about their openness and interpretability. Explainable AI (XAI), a subfield of machine learning that makes models more intelligible to users, is advancing in key applications where decisions can have a major influence on individuals and society. We begin by discussing black-box models' core issues, which can cause distrust and ethical issues by hiding the decision-making process. We show how model transparency has improved by analysing modern methods including model-agnostic methods, interpretable models, and visualisation tools. LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) can help stakeholders understand how input features affect outcomes. We also emphasise the need of domain expertise in designing successful interpretability methods and user-centric explanations customised to stakeholders' demands. XAI's effects on regulatory compliance, ethical AI practices, and stakeholder confidence are investigated, especially under governance frameworks that require AI accountability and transparency. Finally, we propose developing standardised explainability evaluation metrics, including human factors in interpretability frameworks, and exploring hybrid models that balance accuracy and transparency to bridge the gap between AI advancements and interpretability. This work promotes solutions that empower people to make informed decisions based on trustworthy, interpretable AI systems, contributing to the ethical deployment of AI technologies.