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Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
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Vol. 8, Special Issue 1 (2019)
Ethical considerations in the application of machine learning for infectious disease surveillance: A theoretical overview
Author(s):
Prabhat Tiwari, Shakun Garg and Ashish Jain
Prabhat Tiwari, Shakun Garg and Ashish Jain
Abstract:
The rapid advancements in machine learning (ML) technologies have propelled their integration into various domains, including public health. In recent years, the application of ML in infectious disease surveillance has garnered significant attention due to its potential to enhance early detection, monitoring, and response to emerging health threats. However, the ethical implications associated with the use of ML in this context warrant thorough examination.
The rapid advancements in machine learning (ML) technologies have propelled their integration into various domains, including public health. In recent years, the application of ML in infectious disease surveillance has garnered significant attention due to its potential to enhance early detection, monitoring, and response to emerging health threats. However, the ethical implications associated with the use of ML in this context warrant thorough examination.
This review paper provides a comprehensive theoretical overview of the ethical considerations inherent in deploying ML for infectious disease surveillance. It delves into the intersection of technology and public health, emphasizing the need for a balanced approach that maximizes the benefits of ML while minimizing potential risks and ethical pitfalls.
The ethical considerations addressed encompass several key dimensions. First and foremost is the issue of data privacy and security. As ML relies heavily on vast datasets for training and analysis, ensuring the confidentiality of sensitive health information is paramount. The paper explores strategies such as anonymization, encryption, and secure data sharing protocols to mitigate privacy concerns.
Another critical dimension is transparency and interpretability in ML algorithms. The opaque nature of some machine learning models poses challenges in understanding their decision-making processes, which may impact public trust. The review discusses the importance of developing interpretable models and implementing transparent practices to foster accountability and acceptance within the public health community and among the general population.
Additionally, the paper addresses the potential biases embedded in ML algorithms, which may exacerbate existing health disparities. It highlights the importance of designing and validating models with diverse and representative datasets to prevent unintended consequences and ensure equitable outcomes in infectious disease surveillance.
Furthermore, the ethical implications of decision-making autonomy granted to ML systems are explored. Striking a balance between human oversight and automated decision-making is crucial to avoid undue reliance on algorithms and to maintain accountability in public health interventions.
Pages: 14-18 | 175 Views 93 Downloads
How to cite this article:
Prabhat Tiwari, Shakun Garg and Ashish Jain. Ethical considerations in the application of machine learning for infectious disease surveillance: A theoretical overview. The Pharma Innovation Journal. 2019; 8(1S): 14-18. DOI: 10.22271/tpi.2019.v8.i1Sa.25236
Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
Main Menu
Special Issue
Copyright Form
Identifier
The Pharma Innovation Journal
