📂 Difficulty in Data Acquisition

As AI models become increasingly tailored to specific industries, their training demands vast amounts of high-quality, industry-specific data. However, access to diverse and authentic data is hindered by regulatory, privacy, and commercial sensitivity constraints. This creates a major barrier to unlocking AI’s full potential in transforming industry productivity.

The current state of data collection is further complicated by the absence of unified standards for data gathering and classification, leading to inefficiencies in both the collection process and dataset management. Datasets circulating today often suffer from biases, as samples fail to represent the true diversity of the real world. This results in poor model performance in certain scenarios or with specific groups, sometimes even producing discriminatory outcomes. The solution lies in creating more comprehensive and balanced datasets, while applying debiasing techniques to address data imbalances.

While crowdsourcing has shown promise as a potential solution, the challenge remains in providing sufficient incentives for individuals to contribute and share their data. Without a decentralized structure and proper incentives, the system will continue to face limitations.