Explainable Artificial Intelligence (XAI) for Trustworthy and Responsible AI Systems

EasyChair Preprint 14441

Abstract

As artificial intelligence (AI) systems become increasingly integral to decision-making across various domains, ensuring their trustworthiness and ethical operation has become paramount. This research investigates the development and implementation of Explainable Artificial Intelligence (XAI) techniques to enhance transparency, accountability, and fairness in AI systems. XAI aims to make the decision-making processes of AI models interpretable and understandable to human users, thereby fostering trust and enabling responsible AI deployment. The study explores various XAI methodologies, including model-agnostic techniques, interpretable models, and post-hoc explanations, and evaluates their effectiveness in complex, real-world scenarios. By providing clear and actionable insights into how AI systems reach their conclusions, XAI addresses critical challenges such as bias detection, ethical compliance, and user trust. The research also examines the balance between model accuracy and explainability, aiming to optimize AI performance without compromising interpretability. The findings underscore the importance of XAI in creating AI systems that are not only powerful and efficient but also aligned with societal values and ethical standards.As artificial intelligence (AI) systems become increasingly integral to decision-making across various domains, ensuring their trustworthiness and ethical operation has become paramount. This research investigates the development and implementation of Explainable Artificial Intelligence (XAI) techniques to enhance transparency, accountability, and fairness in AI systems. XAI aims to make the decision-making processes of AI models interpretable and understandable to human users, thereby fostering trust and enabling responsible AI deployment.

Keyphrases: Accountability, Ethical AI, Explainable Artificial Intelligence, Model Interpretability, Responsible AI, Trustworthy AI, XAI, bias detection, fairness, transparency

@booklet{EasyChair:14441,
  author    = {Dylan Stilinki},
  title     = {Explainable Artificial Intelligence (XAI) for Trustworthy and Responsible AI Systems},
  howpublished = {EasyChair Preprint 14441},
  year      = {EasyChair, 2024}}