Integration of Artificial Intelligence in Cyber-Physical Systems

Abstract

This study on the integration of Artificial Intelligence in cyber-physical systems advances Socio-Technical Systems theory, offering crucial insights for policymakers to formulate ethical guidelines and targeted interventions addressing human factors, ultimately shaping responsible innovation in the evolving landscape of AI integration. The main objective of this study was to explore the integration of Artificial Intelligence (AI) in cyber-physical systems. The study was anchored on the Socio-Technical Systems (STS) Theory. The study conducted a thorough review and synthesis of diverse scholarly works on integration of Artificial Intelligence in cyber-physical systems, aiming to gain insights into key theories, methodologies, findings, and gaps in the existing body of knowledge. This study revealed significant advancements in efficiency and adaptability across sectors like manufacturing and transportation. Studies by Smith et al. (2012) and Li et al. (2015) showcased the broad applicability of AI, leading to reduced downtime and optimized traffic flow. Additionally, ethical considerations, as highlighted by Chen et al. (2014), emphasized the need for careful addressing of issues such as data privacy and algorithmic bias. The study underscores the promising technological advancements in AI integration while emphasizing the critical importance of responsible and ethical deployment, prompting the ongoing development of guidelines for sustainable implementation. The findings from studies on AI integration in Cyber-Physical Systems (CPS) showcase transformative potential across industries, demonstrating positive impacts on efficiency and adaptability. However, ethical challenges, including data privacy and algorithmic bias, emphasize the need for a responsible AI integration, warranting ongoing attention and guideline development. In terms of contributions, the study advances Socio-Technical Systems theory and offers valuable insights for ethical guidelines and regulatory frameworks. It highlights the importance of addressing human factors in AI adoption, providing guidance for policymakers promoting AI integration within organizations and communities. Overall, these contributions form a solid foundation for advancing theoretical frameworks and shaping policy recommendations in the evolving landscape of AI integration in Cyber-Physical Systems.