ETHICAL CONSIDERATIONS IN BIG DATA-ENHANCED AI: A COMPREHENSIVE ANALYSIS
DOI:
https://doi.org/10.53555/ephijer.v6i3.101Keywords:
Data Challenges, Data Opportunities, Artificial Intelligence (AI), Big Data, Machine Learning, Data Quality, Data Privacy, Data Governance, Data Bias, Data Ethics, Data Management, Data Analysis, Predictive Modeling, Real-time Decision-makingAbstract
"Ethical Considerations in Big Data-Enhanced AI: A Comprehensive Analysis" provides a detailed examination of the ethical dimensions surrounding the integration of Big Data and Artificial Intelligence (AI). As these two transformative technologies become increasingly intertwined, it is essential to address the ethical challenges and implications they present. This research paper commences by elucidating the fundamental concepts of Big Data and AI, emphasizing the importance of responsible data use and ethical AI development. It explores the potential ethical issues that arise from the collection, storage, and analysis of vast datasets, as well as the ethical considerations inherent to AI algorithm design, bias mitigation, and transparency. Through case studies and real-world examples, this analysis highlights the importance of fairness, privacy, accountability, and transparency in the context of Big Data-enhanced AI. It also underscores the need for appropriate regulatory frameworks and guidelines to ensure that the benefits of this technological convergence are equitably distributed and that individual rights and societal values are preserved.
References
. N. Norori, Q. Hu, F. M. Aellen, F. D. Faraci, and A. Tzovara, "Addressing bias in big data and AI for health care: A call for open science," Patterns, vol. 2, no. 10, 2021.
. Y. Duan, J. S. Edwards, and Y. K. Dwivedi, "Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda," International journal of information management, vol. 48, pp. 63-71, 2019.
. J. Car, A. Sheikh, P. Wicks, and M. S. Williams, "Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom," vol. 17, ed: BioMed Central, 2019, pp. 1-5.
. S. A. Bhat and N.-F. Huang, "Big data and ai revolution in precision agriculture: Survey and challenges," IEEE Access, vol. 9, pp. 110209-110222, 2021.
. G. Hasselbalch, Data ethics of power: a human approach in the big data and AI era. Edward Elgar Publishing, 2021.
. M. D'Arco, L. L. Presti, V. Marino, and R. Resciniti, "Embracing AI and Big Data in customer journey mapping: From literature review to a theoretical framework," Innovative Marketing, vol. 15, no. 4, p. 102, 2019.
. L. Surya, "An exploratory study of AI and Big Data, and it's future in the United States," International Journal of Creative Research Thoughts (IJCRT), ISSN, pp. 2320-2882, 2015.
. M. Muniswamaiah, T. Agerwala, and C. C. Tappert, "Federated query processing for big data in data science," in 2019 IEEE International Conference on Big Data (Big Data), 2019: IEEE, pp. 6145-6147.
. S. Strauß, "From big data to deep learning: a leap towards strong AI or ‘intelligentia obscura’?," Big Data and Cognitive Computing, vol. 2, no. 3, p. 16, 2018.
. Y. Chen, "IoT, cloud, big data and AI in interdisciplinary domains," vol. 102, ed: Elsevier, 2020, p. 102070.
. S. Wachter and B. Mittelstadt, "A right to reasonable inferences: re-thinking data protection law in the age of big data and AI," Colum. Bus. L. Rev., p. 494, 2019.
. M. Kantarcioglu and F. Shaon, "Securing big data in the age of AI," in 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), 2019: IEEE, pp. 218-220.
. M. C. Elish and D. Boyd, "Situating methods in the magic of Big Data and AI," Communication monographs, vol. 85, no. 1, pp. 57-80, 2018.
