Applications utilizing Artificial Intelligence (AI) require large data sets for training to ensure they're accurate and can handle diverse scenarios. Unfortunately, traditional data collection processes have many hurdles to overcome, which can reduce efficiency, increase costs, and lower data quality.
However, decentralized data collection presents a solution. It is set to revolutionize the industry by merging data collection with an immutable distributed ledger known as the blockchain. This merging of technologies will enable participants to work together without a central authority, providing manipulation resistance and enhanced privacy.
Now that we have a rough idea of the limitations present in the data collection space and how a decentralized approach could help overcome them, let’s dive deeper.
There are several areas where traditional data collection methods lack effectiveness and could cause issues when used for models with real-world applications:
The manipulation of data is a substantial issue in traditional systems. Whether it’s bad actors intentionally providing false data, AI-generated content affecting data, or malicious attacks designed to impact model performance, manipulation in current data collection processes can lead to significant losses and time-consuming problems.
Data collection in the current sense often experiences bias as systems can underrepresent specific demographics, which can lead to lackluster performance in real-world settings. Additionally, large data sets usually come with significant costs that can price out small start-up projects, making the need for a more cost-effective solution apparent.
Current data collection systems struggle to comply with privacy regulations like the General Data Protection Regulation (GDPR), which can lead to legal concerns. According to a report from Pew Research, up to 73% of Americans feel they have no control over how their data is used, which creates a division between consumers and companies.
Maintaining data quality is critical to train AI models effectively. However, factors like bias, issues with labeling, and redundant information in data sets reduce quality. As a result, they require additional processing power to validate or clean, which leads to a notable increase in costs.
Decentralized data collection tools integrate the blockchain, a technology common in cryptocurrency projects, to act as a secure record of transactions and information. It is immutable and cannot be manipulated, making it far easier to identify fabricated data and helping to improve overall data quality and model performance.
The integration of the blockchain results in a distributed network of devices referred to as nodes, which are responsible for storing and managing data. Each node can make decisions regarding the authenticity and availability of data independently without relying on a central authority, which promotes greater efficiency.
On top of improving efficiency, integrating the blockchain has additional benefits. Instead of gathering data from a single provider, decentralized systems democratize data sourcing, enabling businesses to collect information directly from participants. This approach has key advantages, such as lowered costs, improved privacy, and increased quality.
OORT, for example, a cloud for decentralized AI, offers a community-powered data collection service dubbed “DataHub”. By utilizing a decentralized network, OORT’s DataHub can eliminate intermediaries, which enhances security, transparency, and participant control in large-scale data collection and labeling.
The service incentivizes people from around the world to contribute to data collection. This results in a broader pool of sources, which helps OORT DataHub create diverse and representative data sets geared to real-world use cases. By catering to worldwide users, this approach facilitates global participation at lower prices and will help advance AI and machine learning model training in a cost-effective way.
Through its use of blockchain technology, OORT DataHub can reduce the risks of manipulation, helping it foster a transparent and fair foundation for AI development. Decentralized systems like this are paving the way for ethical data collection in artificial intelligence and help to keep the industry socially responsible and aligned with users' best interests.
As we look to the future, decentralized data collection seems poised to become the go-to method of creating advanced data sets for real-world applications. It offers improvements in privacy, ethics, cost, and data quality versus centralized methods while helping to build a fair and just ecosystem that treats participants with respect.
While decentralized data collection is still in its infancy, projects like OORT DataHub show the world its potential and lay the groundwork for the next generation of ethical and high-performance AI and machine learning models.