Enhancing Explainability and Ethical Decision-Making in AI with a Hybrid Approach of Reinforcement Learning and Attention Mechanisms

Enhancing Explainability and Ethical Decision-Making in AI with a Hybrid Approach of Reinforcement Learning and Attention Mechanisms
  IJETT-book-cover           
  
© 2025 by IJETT Journal
Volume-73 Issue-6
Year of Publication : 2025
Author : R. Senthil Kumar, Selvanayaki Kolandapalayam Shanmugam, J. Lokeshwari
DOI : 10.14445/22315381/IJETT-V73I6P107

How to Cite?
 R. Senthil Kumar, Selvanayaki Kolandapalayam Shanmugam, J. Lokeshwari, "Enhancing Explainability and Ethical Decision-Making in AI with a Hybrid Approach of Reinforcement Learning and Attention Mechanisms," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.65-75, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P107

Abstract
This paper introduces a hybrid approach combining Reinforcement Learning (RL) and Attention Mechanisms to enhance AI systems' explainability and ethical decision-making. In high-stakes fields such as healthcare and autonomous vehicles, making accurate decisions and ensuring that they are transparent and fair is crucial. An Explainable AI (XAI) framework is proposed to offer insights into how decisions are made while helping to infuse ethics concerns, such as fairness and mitigation of bias. The approach utilizes RL in decision-making and Attention Mechanisms to emphasize what is important when making decisions-furthermore, the ethical decision layer guards against providing biased outputs. The result shows that the model balances good performance with clear, ethical explanations, moving toward truly trusted AI in high-stakes applications.

Keywords
Attention mechanisms, Decision-making, Explainability, Fairness, Reinforcement learning.

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