The use of manual labour and physically demanding building techniques is a hallmark of the construction industry. However, recent technological developments have given rise to a rising tendency in the construction industry towards digitalization and data-driven decision-making. The purpose of this study is to investigate how data engineering and computing are used in the construction sector and how they affect building methods. The study depends on a thorough analysis of the literature, which includes 26 pertinent papers from scholarly publications and business reports. The results imply that computing and data engineering can dramatically enhance building procedures and results. Particularly, these technologies make project planning, resource allocation, and risk management more precise and effective. The use of data can also result in more accountability and transparency in building projects, which can save costs and enhance overall project performance. The study also looks at the challenges involved in applying data engineering and computation in the construction sector, including problems with data security, privacy, and quality. The study also emphasises the requirement for more thorough data management plans as well as the creation of industry-wide standards and best practices. The results have important ramifications for experts in the field, academics, and decision-makers, and they can guide future initiatives to incorporate data-driven methods into the construction sector.
This paper explores the ethical dilemmas associated with data and science engineering, with a focus on privacy breaches, algorithmic biases, and the need for transparency. With the increasing reliance on data-driven decision making and machine learning algorithms, the ethical implications of these technologies have become a pressing issue in various sectors. The study aimed to identify the most significant ethical concerns, analyze their impact on society, and provide solutions to address these issues. The research utilized a systematic review of 18 studies to identify the key ethical issues in data and science engineering. The findings revealed that privacy breaches, algorithmic biases, and lack of transparency were the most prevalent ethical concerns. These issues can have significant implications for individuals and groups, including discrimination, loss of autonomy, and reputational harm. The study also identified vulnerable groups, such as marginalized communities, who may be disproportionately affected by these issues. To address these ethical concerns, the study proposed several solutions, including the development of ethical guidelines, increased transparency and accountability, and the use of diverse and representative datasets. The solutions were informed by the literature review, case studies, and analysis of real-world examples. The study also assessed the feasibility of implementing these solutions and highlighted potential barriers to implementation.
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