Open Access

ARTICLE

The Future of Artificial Intelligence in the Face of Data Scarcity

Hemn Barzan Abdalla^1,*, Yulia Kumar², Jose Marchena², Stephany Guzman², Ardalan Awlla³, Mehdi Gheisari⁴, Maryam Cheraghy¹

1 Department of Computer Science, Wenzhou-Kean University, Wenzhou, 325015, China
2 Department of Computer Science and Technology, Kean University, Union, NJ 07083, USA
3 Department of Computer Science, Cihan University Sulaimaniya, Sulaymaniyah, 46001, Iraq
4 Institute of Artificial Intelligence, Shaoxing University, Shaoxing, 312010, China

* Corresponding Author: Hemn Barzan Abdalla. Email:

Computers, Materials & Continua 2025, 84(1), 1073-1099. https://doi.org/10.32604/cmc.2025.063551

Received 17 January 2025; Accepted 28 March 2025; Issue published 09 June 2025

Abstract

Dealing with data scarcity is the biggest challenge faced by Artificial Intelligence (AI), and it will be interesting to see how we overcome this obstacle in the future, but for now, “THE SHOW MUST GO ON!!!” As AI spreads and transforms more industries, the lack of data is a significant obstacle: the best methods for teaching machines how real-world processes work. This paper explores the considerable implications of data scarcity for the AI industry, which threatens to restrict its growth and potential, and proposes plausible solutions and perspectives. In addition, this article focuses highly on different ethical considerations: privacy, consent, and non-discrimination principles during AI model developments under limited conditions. Besides, innovative technologies are investigated through the paper in aspects that need implementation by incorporating transfer learning, few-shot learning, and data augmentation to adapt models so they could fit effective use processes in low-resource settings. This thus emphasizes the need for collaborative frameworks and sound methodologies that ensure applicability and fairness, tackling the technical and ethical challenges associated with data scarcity in AI. This article also discusses prospective approaches to dealing with data scarcity, emphasizing the blend of synthetic data and traditional models and the use of advanced machine learning techniques such as transfer learning and few-shot learning. These techniques aim to enhance the flexibility and effectiveness of AI systems across various industries while ensuring sustainable AI technology development amid ongoing data scarcity.

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Keywords

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