Data and science engineering: The ethical dilemma of our time-exploring privacy breaches, algorithmic biases, and the need for transparency
1 Israel Institute of Technology and a master's degree in building construction from Georgia Institute of Technology, Georgia.
2 KU Leuven in Belgium, Belgium.
3 Fatima Jinnah Women University and also graduated from the University of People America. America.
Research Article
World Journal of Advanced Research and Reviews, 2023, 18(01), 762–768
Article DOI: 10.30574/wjarr.2023.18.1.0677
Publication history:
Received on 08 March 2023; revised on 15 April 2023; accepted on 18 April 2023
Abstract:
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.
Keywords:
Data; Science engineering; Ethics; Privacy; Transparency; Bias; Algorithms
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0