Ethical and Responsible AI: A Comprehensive Review of Principles, Methods, and Tools - Divyansh Rajat; Akshita; Jaspreet Kaur (International Journal of Information Security Engineering, 2026)
Quick development of artificial intelligence (AI) has revolutionized a number of industries, including healthcare, banking, and government, by providing creative answers to challenging issues. However, there are serious ethical issues with growing integration of AI into crucial decision-making processes, including prejudice, a lack of transparency, abuses of data privacy, and accountability gaps. A systematic strategy that incorporates technical solutions, legal frameworks, and ethical standards is needed to address these issues. With an emphasis on fundamental concepts like equity, responsibility, transparency, privacy, and inclusion, this study offers a thorough analysis of ethical AI. It examines the strategies and resources created to guarantee the responsible application of AI, such as explainability tactics, algorithms that improve fairness, and privacy-preserving measures like federated learning and differential privacy. The study also looks into federated learning frameworks that support ethical compliance as well as AI governance solutions like AI Fairness 360, SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and others. Notwithstanding developments, problems, including moral trade-offs, scalability problems, and inconsistent regulations, still exist. In order to create strong, moral AI systems, this study emphasizes the value of interdisciplinary cooperation between academics, decision makers, and business executives. Improving AI interpretability, tackling socio-technical biases, and promoting international collaboration on AI ethics are some future research avenues. In order to ensure justice, security, and accountability in AI- driven applications, stakeholders seeking to align AI technologies with ethical norms may find this paper to be a useful resource.
Keywords: AI governance; Explainability; Privacy-preserving AI; ethical AI; fairness; responsible AI