Software Engineering’s Key Role in AI Content Trustworthiness

Abstract

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. It can also be defined as the science and engineering of making intelligent machines, especially intelligent computer programs. In recent decades, there has been a discernible surge in the focus of the scientific and government sectors on reliable AI. The International Organization for Standardization, which focuses on technical, industrial, and commercial standardization, has devised several strategies to promote trust in AI systems, with an emphasis on fairness, transparency, accountability, and controllability. Therefore, this paper aims to examine the role of Software Engineering in AI Content trustworthiness. A secondary data analysis methodology was used in this work to investigate the crucial role that software engineering plays in ensuring the accuracy of AI content. The dataset was guaranteed to contain reliable and comprehensive material relevant to our inquiry because it was derived from peer-reviewed publications. To decrease potential biases and increase data consistency, a rigorous validation process was employed. The findings of the paper showed that lawful, ethical, and robust are the fundamental components of reliable Artificial Intelligence. The criteria for Reliable Artificial Intelligence include Transparency, Human agency and oversight, technical robustness and safety, privacy and data governance, diversity, non-discrimination, fairness, etc. The functions of software engineering in the credibility of AI content are Algorithm Design and Implementation, Data Quality and Preprocessing, Explainability and Interpretability, Ethical Considerations and Governance, User feedback, and Iterative Improvements among others. It is therefore essential for Software engineering to ensure the dependability of material generated by AI systems at every stage of the development lifecycle. To build and maintain reliable AI systems, engineers must address problems with data quality, model interpretability, ethical difficulties, security, and user input.