The nascent digital twin (hereinafter termed as DT) is a computer program that analyses and controls the physical system or asset as well as simulates it in a real-world condition through data transmission. It is akin to the pairing technology used by NASA to unknot the operational, maintenance, and repair issues of systems that weren't physically nearby. Therefore, the technology helps to predict how the physical system or asset reacts under changing circumstances. Consequently, in the chapter, the authors discuss, analyze, and critically evaluate the ethico-legal parameters underlying the application of the DT technology.
TopIntroduction
The 21st century, an era flourishing with the rise of the most trending and disruptive technologies, evidently has brought massive relevance to the technology of digital twins (DT). DT is a recent taxonomy but conceptually the technological foundation dates to the 1960’s. Initially, it revolutionized the aerospace industry and latterly, DT’s significance has grown multiple folds with the advent of Cybernetics, Big data, Artificial Intelligence and Robotics. It is presumed that the growing application of Nanotechnology (especially the Internet of Bio-Nano Things) (Almazrouei, Shubair, and Saffre, 2018). has potential like Midas’s touch in the health sector, military applications, and environmental fields. The terms ‘Virtual Twin’ (Stark and Damerau, 2019). are synonymous while ‘Virtual Prototype’, ‘Hybrid Twin Technology’, and ‘Digital Asset Management are relative terms to DT (Stark and Damerau, 2019).
Supposedly, DT can be called a virtual clone (Palla, 2022; Gartner, 2020). of a physical object (e.g., a windmill) that virtually simulates. Such a simulation can take place even before the real object is designed or deployed. This technology has been deployed upon real objects which weren’t physically proximate at compound systemic stages by various groups from time to time for assessing, simulating, and controlling it. In short, a DT is a digital duplicate of a physical object or asset. The present Industry 4.0 phase looks at this technology as a key to unlocking unrequited space between the physical and virtual worlds (Althavale and Althavale, 2021).
Undoubtedly, DT is a remarkable edge of technological evolution that capacitates technological perfection. Thus, the scope of DT is wide-ranging and diverse. It discharges accurate predictions of the real object in real-time concomitantly. It not only alongside showcases the exact performance of the real asset, but also it is a means to comprehend the performance of the asset model, by predicting its behavioral pattern and optimizing its operations and services.
The DT technology has been applied to simple infrastructures and has come out with brilliant benefits and precision at the manufacturing level. However, DT is predicted to have massive potential to manage machine infrastructures under not only complex systems but also multiple systems at multiple levels at the same time. Perhaps DT technology knows no bounds with the advent of AI, Cybernetics, Nanotechnology, and other disruptive technologies. It is spreading wings across multiple objects which astonishingly rise beyond machines and their designs. There are fundamental attempts currently in progress that involves “What Ifs” and DT to raise the sustainability of the Planet, enhance the quality of life of humans by breakthrough into bioscience as well as implement smart city/nation for effective governance with the extensive use of DT.
Every new technology comes with its pros and cons and DT is not an exception to it. A technology standing alone is generally harmless, the concern begins while implementation and further blending with other technologies. Hence, in the case of DT obvious challenges, both ethical and legal come to the shore. The indicative potential of DT creates an influx of competition by companies, industries, government concerns, and agencies. It is inevitable to face fundamental trials and confines when complex dynamic model systems (like geographical twins, weather, climate, life, behavior, health, etc.) backed by uncertainty are built. On one hand, the gross machine learning infrastructures grasp trillion information and pose challenges like data privacy, data ownership; data interoperability; confidentiality, and liability concerns. On the other hand, the growing complexities pose a conflict of interest and intensify security challenges. The objective of this chapter is to throw light upon the regulatory challenges regarding the mounting use of DT technology and attract the attention of policymakers, lawyers, engineers, academicians, researchers, and scholars so that the technology is judiciously put to use.