Decentralized Data Curation: The Key to Ethical AI

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In today’s digital world, a select group of tech giants such as Google, Facebook (Meta), and Amazon possess an unparalleled grip on the most prized asset of our times: information generated by users. By accumulating, saving, and commercializing the personal details of countless individuals, these corporations have amassed colossal data realms. This concentration of data hinders competition, restricts progress, and fosters isolated spaces where access is granted to only a select few.

In simpler terms, while Decentralized Physical Infrastructure Networks (DePINs) have encouraged users to engage in decentralized infrastructure, one area that still lacks attention is data management. This is where Data Curation Networks (DCNs), a type of decentralized system designed to collect and manage data directly from users, come into play. DCNs can potentially solve the issue of data confinement by breaking free from these silos.

DCNs (Decentralized Data Networks) present an exceptional prospect for the burgeoning AI market. AI relies heavily on top-notch, distinct data sets to perform at its best, and substantial data sets are crucial for model training, system enhancement, and driving future applications. Additionally, DCNs can help alleviate regulatory issues regarding AI bias by generating varied and open human-created datasets.

Decentralizing data collection for AI through data curation networks

As an analyst, I’m witnessing an exciting development: Decentralized Platform for Internet Network (DePIN) has already breached the $50 billion market capitalization, and projections suggest a potential market value of a staggering 3.5 trillion by 2028. This growth trajectory underscores the immense potential of decentralized networks to revolutionize data ownership, giving control back to the users and enabling them to reap the benefits of their contributions. DePIN is spearheading this transformation by transferring data collection from corporate titans into individual hands, fostering a more equitable digital landscape.

With advancements in artificial intelligence, the necessity for varied and top-tier data will become even more crucial. Traditional centralized corporations may struggle to gather the extensive range of data required for numerous AI applications due to their limitations. In contrast to corporate-held datasets, which can be influenced by the user base or confined by the company’s scope, DePIN networks are capable of accessing a vast array of sources. This results in more complete and diverse datasets, which are vital for creating superior, more inclusive AI models, and discovering innovative use case scenarios.

For instance, consider the evolution of self-driving cars. Self-governing systems rely heavily on real-time data about traffic flow, road conditions, and driving habits to operate securely and effectively. In the past, this data was primarily collected by large companies with connected vehicles and road sensors at their disposal. Creating such infrastructure is expensive, demanding both financial investment in infrastructure and extensive labor hours. Instead of shouldering these costs and assembling a workforce for this specific purpose, crypto networks can encourage individuals to transform their devices into data collection stations, passively gathering valuable data during the course of their daily routines. This approach allows for geographically diverse data to be curated more efficiently, yielding organic datasets that are ideal for AI learning purposes.

Autonomous vehicles, like numerous other scenarios, demonstrate the potential of distributed systems in collecting vital information to enhance safety and efficiency. By merging live data from various decentralized resources with artificial intelligence’s analytical capabilities, we can bring about a transformative shift across sectors, such as transportation and healthcare.

Fueling AI innovation while rewarding users

AI models designed to serve human needs must utilize human-generated data as a foundation for training, since it serves as the ultimate source of truth. With the rise of IoT and wearable devices that incorporate computational power and AI-enhanced chips, as well as billions of connected consumer devices like smartphones, edge-based Deep Convolutional Neural Networks (DCNs) could potentially scale massively. This scaling, in turn, would extend their scope and capacity significantly. Data curation processes would become incredibly efficient due to this scaling effect, and the quality of available datasets would be greatly improved.

By using common devices such as smartphones and laptops instead of demanding new hardware investments, commodity-based Decentralized Computing Networks (DCNs) make participation more accessible for people. This approach eliminates many challenges associated with hardware production and distribution, simplifies the signup process, and encourages user involvement at minimal initial cost. In the growing DCN landscape, data sets are frequently amassed by leveraging existing physical infrastructure, bolstered by creative cryptocurrency incentives. For example, certain projects in the web3 domain provide web scraping services through Chrome extensions on personal computers, while others utilize smartphone cameras for mapping purposes. This demonstrates how commodity-based DCNs decrease the barriers to entry.

In this modern approach, users are the primary recipients of advantages. They obtain ownership over their information, earn income from donating to decentralized platforms, and could potentially profit from AI-powered advancements that these networks foster. This not only promotes a fairer digital landscape but also invites increased involvement in the data marketplace, meaning that AI developments are shaped by the requirements and input of average individuals instead of being driven solely by the financial ambitions of a select group of big corporations.

This article was co-authored by Alireza Ghods and Tommy Eastman.

Alireza Ghods & Tommy Eastman

Alireza Ghods serves as CEO and one of the founders of NATIX. He holds a Ph.D. in the field of geospatial positioning, and boasts significant experience as a research and development engineer within the geospatial data industry, autonomous vehicles, and creating real-time dynamic maps. Prior to his role at NATIX, he oversaw Internet of Things (IoT) and Blockchain initiatives in Europe for PWC.

 

Tommy Eastman, who serves as the research lead at Plaintext Capital, dedicated two years to spearheading projects centered on decentralized AI and DePIN as a Software Engineer at Foundry. Prior to that, he gained valuable experience in AI and practical problem-solving through machine learning for object detection at L3Harris, a defense contractor.

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2025-01-20 16:16