Explainable Artificial Intelligence (XAI) as a foundation for trustworthy artificial intelligence

Abstract

The rapid integration of artificial intelligence (AI) into various sectors necessitates a focus on trustworthiness, characterized by principles such as fairness, transparency, accountability, robustness, privacy, and ethics. Explainable AI has become essential and central to the achievement of trustworthy AI by answering the "black box" nature of top-of-the-line AI models through its interpretability. The research further develops the core principles relating to trustworthy AI, providing a comprehensive overview of important techniques falling under the XAI rubric, among them LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). It follows with how this would make agents more trustworthy, better at cooperating with humans, and more compliant with regulations. This will be followed by the integration of XAI with other AI paradigms deep learning, reinforcement learning, and federated learning by contextualizing them in the light of a discussion on the performance-transparency trade-off. This is followed by a review of currently developed regulatory and policy frameworks guiding ethical AI use. Such applications of XAI in domains relevant to healthcare and finance will be presented, demonstrating its impact on diagnosis, trust earned from patients, risk management, and customer engagement. Emerging trends and future directions in XAI research include sophisticated techniques for explainability, causal inference, and ethical considerations. Technical complexities, scalability, and striking a balance between accuracy and interpretability are some of the challenges.

Topics & Concepts