The Analytics Paradigm: Balancing Innovation and Ethics in a Data-Centric World

Teena Choudhary

doi:10.32628/CSEIT241051034

Authors

Teena Choudhary Infosys Ltd, USA Author

DOI:

https://doi.org/10.32628/CSEIT241051034

Keywords:

Data Analytics, Societal Transformation, Ethical Implications, Innovation, Decision-Making

Abstract

This article examines data analytics's profound and multifaceted influence on contemporary society, exploring its transformative impact across various sectors and the challenges it presents. By synthesizing current research and case studies, we demonstrate how data analytics enhances decision-making through personalization and predictive insights, drives innovation in business and technology, and improves public services, particularly in healthcare and urban planning. The article also critically addresses the ethical implications and privacy concerns associated with the proliferation of data-driven approaches, including data security, algorithmic bias, and fairness. As data analytics continues to reshape societal structures and individual experiences, this article argues for a balanced approach that maximizes its potential benefits while mitigating risks. Our analysis concludes by considering the long-term societal implications of this data revolution, emphasizing the need for adaptive policies, education, and ethical frameworks to guide the future development and application of data analytics in an increasingly connected world.

Downloads

Download data is not yet available.

References

J. M. Hofman, A. Sharma, and D. J. Watts, "Prediction and explanation in social systems," Science, vol. 355, no. 6324, pp. 486-488, 2017. [Online]. Available: https://www.science.org/doi/10.1126/science.aal3856 DOI: https://doi.org/10.1126/science.aal3856

F. Provost and T. Fawcett, "Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking," O'Reilly Media, Inc., 2013. [Online]. Available: https://www.oreilly.com/library/view/data-science-for/9781449374273/

N. J. Schork, "Personalized medicine: Time for one-person trials," Nature, vol. 520, no. 7549, pp. 609-611, 2015. [Online]. Available: https://www.nature.com/articles/520609a DOI: https://doi.org/10.1038/520609a

A. Rajkomar, "Scalable and accurate deep learning with electronic health records," npj Digital Medicine, vol. 1, no. 18, 2018. [Online]. Available: https://www.nature.com/articles/s41746-018-0029-1

C. A. Gomez-Uribe and N. Hunt, "The Netflix Recommender System: Algorithms, Business Value, and Innovation," ACM Transactions on Management Information Systems, vol. 6, no. 4, pp. 1-19, 2015. [Online]. Available: https://dl.acm.org/doi/10.1145/2843948 DOI: https://doi.org/10.1145/2843948

E. Ayday, J. L. Raisaro, J. P. Hubaux, and J. Rougemont, "Protecting and Evaluating Genomic Privacy in Medical Tests and Personalized Medicine," Proceedings of the 12th ACM Workshop on Privacy in the Electronic Society, pp. 95-106, 2013. [Online]. Available: https://dl.acm.org/doi/10.1145/2517840.2517843 DOI: https://doi.org/10.1145/2517840.2517843

J. Wiens, S. Saria, M. Sendak, M. Ghassemi, V. X. Liu, F. Doshi-Velez, K. Jung, K. Heller, D. Kale, M. Saeed, P. N. Ossorio, S. Thadaney-Israni, and A. Goldenberg, "Do no harm: a roadmap for responsible machine learning for health care," Nature Medicine, vol. 25, pp. 1337–1340, 2019. [Online]. Available: https://www.nature.com/articles/s41591-019-0548-6 DOI: https://doi.org/10.1038/s41591-019-0548-6

A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, "Internet of Things for Smart Cities," IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22-32, 2014. [Online]. Available: https://ieeexplore.ieee.org/document/6740844 DOI: https://doi.org/10.1109/JIOT.2014.2306328

P. Voigt and A. von dem Bussche, "The EU General Data Protection Regulation (GDPR): A Practical Guide," Springer International Publishing, 2017. [Online]. Available: https://link.springer.com/book/10.1007/978-3-319-57959-7 DOI: https://doi.org/10.1007/978-3-319-57959-7

N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, "A Survey on Bias and Fairness in Machine Learning," ACM Computing Surveys, vol. 54, no. 6, pp. 1-35, 2021. [Online]. Available: https://dl.acm.org/doi/10.1145/3457607 DOI: https://doi.org/10.1145/3457607

E. J. Topol, "High-performance medicine: the convergence of human and artificial intelligence," Nature Medicine, vol. 25, pp. 44–56, 2019. [Online]. Available: https://www.nature.com/articles/s41591-018-0300-7 DOI: https://doi.org/10.1038/s41591-018-0300-7

L. Floridi, J. Cowls, M. Beltrametti, R. Chatila, P. Chazerand, V. Dignum, C. Luetge, R. Madelin, U. Pagallo, F. Rossi, B. Schafer, P. Valcke, and E. Vayena, "AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations," Minds and Machines, vol. 28, pp. 689–707, 2018. [Online]. Available: https://link.springer.com/article/10.1007/s11023-018-9482-5 DOI: https://doi.org/10.1007/s11023-018-9482-5