Ethical and Interpretable AI Systems for Decision-Making in Autonomous Infrastructure Management (Published)
As artificial intelligence (AI) systems increasingly govern core infrastructure components, ethical and interpretable decision-making becomes essential to ensuring safety, compliance, and public trust. This paper introduces a unified framework that integrates ethical design principles and explainable AI (XAI) techniques into autonomous infrastructure systems. By embedding human oversight, fairness-aware reinforcement learning, and robust audit mechanisms, our approach enhances transparency in applications such as cloud resource management, cybersecurity enforcement, and load balancing. Real-world use cases and evaluations on a hybrid cloud testbed illustrate that these mechanisms improve fairness and compliance without significantly impacting system performance.
Keywords: Decision Making, Ethical, autonomous infrastructure management, interpretable AI systems
Causal Inference in Data Science: A Framework for Attribution Systems (Published)
This article explores the fundamental principles and applications of causal inference in data science, particularly focusing on attribution systems across business domains. It examines how causal inference methods enable organizations to move beyond traditional correlation to establish more robust attribution frameworks. The article discusses key methodological approaches, including directed acyclic graphs, counterfactual analysis, and machine learning integration, while addressing implementation challenges in real-world business settings. Through analysis of recent research and case studies, the article demonstrates how causal inference techniques enhance decision-making accuracy in marketing, customer analytics, and financial strategies. The article highlights both the theoretical foundations and practical applications of causal inference, emphasizing its role in improving attribution accuracy and business outcomes across various organizational contexts.
Keywords: Decision Making, attribution systems, business analytics, causal inference, machine learning
Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration (Published)
Amplifying the utilization of big data in healthcare analytics through cloud and Snowflake migration presents a significant opportunity to enhance data-driven insights and decision-making in the healthcare sector. This migration makes it easier to move large amounts of healthcare data to the cloud. Applications deployed in could are scalable for in-depth analysis in Health Care industry. The cloud is becoming more popular for storing data and running applications because it can easily grow with your needs, requires little to no management, improves security, and offers budget flexibility. The benefits of the cloud are obvious — once you get there. Moving to the cloud requires planning, strategy, and the right tools for data migration. [1] By using Snowflake’s advanced data warehousing tools, healthcare organizations can smoothly handle and analyze their complex and varied data. This helps them quickly uncover important insights and make better decisions. The shift to cloud technology and Snowflake has the potential to significantly enhance real-time analytics, personalized patient care, and evidence-based decision-making in healthcare. When healthcare organizations leverage big data in a cloud-based setting, they can discover valuable insights from their data, ultimately improving clinical outcomes, operational efficiency, and healthcare delivery. This study explores how the adoption of cloud and Snowflake in healthcare analytics can bring about transformative change and create new possibilities for leveraging data and generating insights in the healthcare sector.
Keywords: Big Data, Cloud Migration, Data Insights, Decision Making, Healthcare Analytics, Real-time Analytics, Snowflake, data security, scalability