ETHICAL DECISION-MAKING MODELS IN ARTIFICIAL INTELLIGENCE

Authors

  • Kaveri Assistant Professor, Department of Master of Computer Application, Guru Nanak Dev Engineering College, Bidar, India
  • Apoorva 2nd Semester, Department of Master of Computer Applications, Guru Nanak Dev Engineering College, Bidar, India
  • Nikhita 2nd Semester, Department of Master of Computer Applications, Guru Nanak Dev Engineering College, Bidar, India

Keywords:

Artificial Intelligence, Ethical Decision-Making, Machine Learning, AI Ethics, Fairness, Accountability, Transparency, Bias Mitigation, Explainable AI (XAI), Autonomous Systems, Responsible AI, Human-AI Interaction, Algorithmic Governance, Ethical Frameworks, Hybrid AI Models

Abstract

The rapid integration of Artificial Intelligence (AI) into real-world applications has introduced significant ethical challenges, particularly in systems that make autonomous or semi-autonomous decisions. This research examines various ethical decision-making models in AI and evaluates their effectiveness in ensuring fairness, accountability, and transparency. The primary objective of this study is to analyze how different ethical frameworks—such as rule-based models, utilitarian approaches, deontological principles, and machine learning-driven methods—can be incorporated into intelligent systems to guide responsible decision-making.

The study adopts a comparative analytical approach to assess the strengths and limitations of each model in practical scenarios, including healthcare diagnostics, autonomous vehicles, and financial decision systems. The findings indicate that rule-based models provide clarity and control but lack adaptability, while data-driven approaches offer flexibility but are prone to bias and lack interpretability. Hybrid models, which combine predefined ethical constraints with learning-based adaptability, demonstrate improved performance in handling complex, real-world ethical dilemmas.

Furthermore, the research highlights critical challenges such as algorithmic bias, lack of transparency, and difficulties in assigning accountability. The results emphasize the need for integrating human oversight and explainability mechanisms into AI systems. In conclusion, the study proposes a multi-layered ethical framework that balances technical efficiency with moral responsibility, ensuring that AI systems align with societal values while maintaining reliability and trustworthiness in decision-making processes.

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Published

01-06-2026

How to Cite

Kaveri, Apoorva, & Nikhita. (2026). ETHICAL DECISION-MAKING MODELS IN ARTIFICIAL INTELLIGENCE. International Educational Journal of Science and Engineering, 9(05), 671–677. Retrieved from https://iejse.com/journals/index.php/iejse/article/view/409

Issue

Vol. 9 No. 05 (2026): SPECIAL ISSUE MAY - NATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING, TECHNOLOGY AND MANAGEMENT

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Articles

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