• Mallikarjunarao Sunke

The lack of data is extremely difficult to deal with when training an Artificial Intelligence (AI) model and will be interesting to see how this problem can be solved in the future. Currently, AI is transforming more and more industry and data scarcity is a major challenge. This research paper discusses important implications with respect to lack of data in AI training, its potential and growth and suggests possible outcomes. Additionally, this study also highlights various ethical implications like consent, privacy, and unbiased laws about deploying AI models under monitored conditions. In parallel, innovative solutions are investigated on aspects that need to be adopted with few-shot learning, transfer learning and data augmentation in order to adapt model to the efficient processes for the utilization of AI with limited resources. Therefore, it aims to work on the best methods and team solutions to make it fair and applicable to address ethical and technical problems of data scarcity. This study also explores potential approaches when addressing data scarcity with the blend of traditional and synthetic models along with using modern ML techniques like few-shot learning and transfer learning. These methods improve efficiency and flexibility of AI models in different sectors to develop sustainable technology amid existing issues of lack of natural data.

Data Scarcity, AI Models, AI Training, Synthetic Data, Generative AI Collaborative Methods.

"Generative AI-Based Synthetic Data Generation for Addressing Data Deficiency", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.a266-a278, September-2026, Available :http://www.jetir.org/papers/JETIR2609031.pdf

"Generative AI-Based Synthetic Data Generation for Addressing Data Deficiency", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppa266-a278, September-2026, Available at : http://www.jetir.org/papers/JETIR2609031.pdf