TRUST-AWARE AI SYSTEMS: IMPROVING TRANSPARENCY IN AUTONOMOUS DECISION-MAKING
Keywords:
Trust-Aware AI, Explainable Artificial Intelligence (XAI), Transparency, Autonomous Decision-Making, Fairness in AI, Human-AI InteractionAbstract
The increasing reliance on autonomous decision-making systems across critical domains has raised significant concerns regarding transparency, reliability, and user trust. Many modern artificial intelligence modelsoperate as complex black boxes, making it difficult for users to understand how decisions are derived. This research focuses on designing a trust-aware artificial intelligence framework that enhances transparency while maintaining high performance. The proposed system integrates explainability techniques, fairness evaluation mechanisms, and confidence-based decision scoring to provide interpretable and accountable outputs. By incorporating methods such as feature attribution, bias detection, and user feedback loops, the system enables users to gain insights into the reasoning behind predictions. Experimental evaluation demonstrates that the proposed approach improves user trust and satisfaction without significantly compromising model accuracy. The results indicate that combining interpretability with measurable trust indicators leads to better human-AI interaction and more responsible deployment of intelligent systems. Furthermore, the framework supports auditability and ethical compliance, making it suitable for applications in healthcare, finance, and automated decision support systems. The study concludes that embedding transparency and trust mechanisms within AI systems is essential for their widespread adoption and long-term sustainability. Future enhancements may focus on real-time adaptability and deeper integration of human-in-the-loop strategies to further strengthen trust and accountability in autonomous systems.
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