Artificial intelligence (AI) can only serve its purpose if it is trained upon quality data. The success of an AI algorithm largely depends on the quality and quantity of the training data used. Accordingly, it shouldn’t come as a surprise that almost 80% of the total time spent building an AI project is allocated to optimizing training data, including steps like accumulation, filtering, and data labeling.
Most AI projects face the uphill task of collecting or acquiring quality data. There are several instances where projects often end up with unlabeled data or low-quality labeled data. While multiple data labeling services have emerged over the past few years, addressing the challenge to a degree, they feature their own set of problems. For instance, the main reasons behind low-quality labeled data are the people, process, or technology used in labeling it.
But what precisely is labeled data?
Data Labeling: The Fuel For AI Models
In the context of AI, labeled data refers to data that is “marked or annotated” to enable a machine learning model to predict the outcome you want. In general, the entire data labeling process usually includes multiple steps, like data annotation, classification, tagging, moderation, and processing.
There are several data labeling approaches that can be employed either independently or in combination. This includes in-house data labeling, outsourcing, crowdsourcing, and using machines (where data is labeled using machine learning algorithms).
Depending on the complexity of the problem, AI projects often use exhaustive labeling processes to convert unlabeled data into the training data they need to teach their AI models which patterns to recognize to generate the desired output.
Of the many available methods, crowdsourcing, which is using a third-party platform to access vast amounts of human workers at once, is one of the most commonly used tactics by projects for labeling data. In recent years, several platforms like Amazon MTurk, Appen Meeta Dash, Labelbox, and Tagtog, among others have emerged as some of the most promising platforms to crowdsource human workers for data labeling.
However, several projects have raised concerns about the data quality offered by crowdsourcing platforms. Take, for instance, the data quality problem with Amazon Mechanical Turk (MTurk) that goes back as far as 2018. Many data researchers suspect that data was being labeled using bots alongside semi and fully-automated code or scripts to assist humans in responding more rapidly to certain datasets.
A portion of the problem was traced back to users from different locations who used VPNs to participate in surveys and questionnaires that weren’t for their locale. Since crowdsourcing platforms offer decent pay to human workers for completing tasks, users often partake in duplicitous activities to generate more income. For example, a bunch of users from different countries can use VPN to enter a data labeling program that requires specific responses from American residents. This leads to lower-quality and nonsensical responses, which, in turn, lowers data quality.
If low-quality data is being submitted, it raises serious questions about the quality assurance process in place. Then again, since most of the existing crowdsourcing platforms for data labeling are heavily centralized, it is almost impossible to assess the quality and the workflow. All of these problems, paired with the meteoric growth of blockchain technology, have paved the way for decentralized and permissionless crowdsourcing solutions.
This is where HUMAN Protocol presents a novel new approach to data labeling by creating an infrastructure that supports permissionless job markets, which simultaneously supply human workers with work and give organizations access to workforces – all of it without any centralized intermediaries.
Facilitating Permissionless Job Markets
HUMAN Protocol enables the creation of distributed marketplaces for tasks across a global network. However, bear in mind that the HUMAN Protocol isn’t a marketplace in itself. Instead, it provides the necessary tools and infrastructure to support decentralized marketplaces.
By design, the HUMAN Protocol is an open-source, decentralized, and automated infrastructure that provides a hybrid framework for organizing, evaluating, and compensating human labor. HUMAN Protocol serves the interest of both workers and employers (requesters). As a result, it can be used across a wide range of use cases, including crowdsourcing and gig-based projects.
Although the HUMAN Protocol has near-universal applicability, it is initially focused on supporting decentralized marketplaces related to machine learning (ML). More specifically, HUMAN Protocol facilitates the collection of huge volumes of quality human annotation data while maintaining optimal service levels.
While the HUMAN Protocol originally emerged from hCAPTCHA, one of the most popular and tested CAPTCHA services on Web 2.0, the platform has since established itself as a totally unique entity by offering the underlying technology to support permissionless job markets in which almost any task – including data labeling – can be crowdsourced.
At present, the HUMAN job market offers video, image, and text annotation markets, where buyers and sellers are matched. The underlying protocol can divide a job (task) across many of these markets and send it to the appropriate Exchanges (the applications that workers use to complete the job). Additionally, it can cross-check the data across job markets to ensure quality.
On top of it, the HUMAN Protocol team has handpicked the best available tools for each job market. They have developed and are continuously optimizing the Exchanges to offer workers everything they need to complete the requested tasks. The protocol also includes tools that maintain end-to-end quality control over the submitted jobs. This effectively means that requesters will receive a more deterministic outcome if similar jobs are fulfilled through the same Exchange.
Finally, compared to heavily centralized and micro-managed platforms, HUMAN Protocol offers a fully open solution that allows a diverse range of projects to leverage its infrastructure. Moreover, it also features the capability to help projects add their own tools to fulfill data labeling requirements more accurately, efficiently, and without any intermediaries. Most importantly, the listing, distribution, and compensation of jobs, alongside millions of micropayments, is automated, thanks to the protocol’s application of blockchain technology to facilitate transactions and settlement in an orderly, reliable, and fair manner.
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