Abstract

As Artificial Intelligence (AI) systems become a component of enterprise systems, the issue of the reliability of such systems is of paramount concern, especially in mission-critical environments, where the price of failure can be high. The key concepts of creating AI platforms that must be reliable and accountable are presented in this paper in this context. It dwells upon the issues that arise when AI is deployed, including the concerns with the problem of explainability, the control of bias, and the safety of its operations. This has been argued to be systems-engineering-based, and the importance of a good design approach was emphasized. The article outlines protective architectural designs, 24/7 surveillance policies, and authentication to maintain transparency, guarantee the audibility, and ensure that AI-generated outcomes remain within the business goals. The work provides a practical methodology for the deployment of responsible AI systems by presupposing the presence of trust as the property of the system, as contrasted with the idealized conception. This approach not only causes AI systems to gain credibility in uncertain and high-stakes conditions, but also preconditions the scaling of AI technologies in such a manner that can result in sustainability in the long-term perspective and governance ethics. The paper highlights the necessity to create AI to pay more attention to accountability and the safety of system operation without impacting performance.