Explainable AI Models for Transparent and Responsible Automated Decision-Making

Kary Främling

Explainable AI Models for Transparent and Responsible Automated Decision-Making

Author(s)	Kary Främling
Country	United States
Abstract	The rapid advancement of Artificial Intelligence (AI) has significantly transformed automated decision-making across healthcare, finance, education, public administration, and industrial management. AI-driven systems are increasingly being used to analyse complex datasets, identify patterns, and generate predictive outcomes that support strategic and operational decisions. However, the growing dependence on automated decision systems has created concerns regarding transparency, accountability, fairness, and user trust. Many advanced AI models, particularly deep learning architectures, function as complex black-box systems where decision processes are difficult for human users to understand. This limitation restricts their acceptance in high-impact domains where explanations and responsible decision-making are essential. Explainable Artificial Intelligence (XAI) has emerged as an important research direction that aims to improve the interpretability and transparency of AI models while maintaining predictive performance. This study examines explainable AI models designed to support transparent and responsible automated decision-making by integrating interpretability techniques, ethical principles, and human-centered evaluation approaches. A simulation-based analytical framework is developed to compare conventional AI models with explainable AI approaches using performance indicators including prediction accuracy, explanation quality, transparency level, and user trust. The findings indicate that explainable AI models provide improved transparency and stronger decision reliability by enabling users to understand the reasoning behind automated recommendations. The study highlights that responsible AI adoption requires a balanced approach where technical performance is combined with accountability, fairness, and meaningful human oversight. The proposed framework contributes to ongoing discussions on trustworthy artificial intelligence by presenting explainability as a fundamental requirement for future automated decision systems.
Keywords	Explainable Artificial Intelligence, Transparent AI, Responsible AI, Automated Decision-Making, AI Governance, Machine Learning Interpretability, Trustworthy Artificial Intelligence.
Field	Engineering
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-01-08

View / Download PDF File

About AIJRI Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@aijri.com

American International Journal of Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Explainable AI Models for Transparent and Responsible Automated Decision-Making

Share this