Trust and Accountability in Agentic AI Systems
DOI:
https://doi.org/10.15680/IJCTECE.2025.0802008Keywords:
Agentic AI, Trust level, System accuracy, Fairness score, Reliability score, Transparency scoreAbstract
This Article examines how explainability, fairness and reliability contribute to user trust in agentic AI systems. The study shows how these guiding principles can be integrated into AI technology to promote transparency and accountability and end up with increased acceptance and dependability. Using the context of real-world case studies, including autonomous vehicles and AI in healthcare, the work identifies the issues and possibilities in incorporating these aspects into AI systems. The study is based on a mixed-method approach of qualitative analysis and data-based evaluation criteria to measure the processes of trust-building. Important conclusions are that the absence of transparency and fairness may be a serious blow to user trust, and systems that take explainability and accountability into consideration are more adopted and satisfactory. The paper ends by giving recommendations to the developers of AI to make emphasis in these areas to make the AI systems trustworthy so as to foster more adaptive and user-friendly AI systems.
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