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AI’s growth engine: Data labeling & collection

Santiago Gorbea

February, 13, 2024

20 min. reading

The AI industry, at the forefront of technological advancement, extends its transformative influence across nearly every sector. As a dynamic growth engine, AI propels businesses forward, unlocking unprecedented insights and transforming operations. Central to this revolution is data, the multifaceted gem fueling AI’s machine learning algorithms. In its raw form, data holds untapped potential, which is fully realized through a journey of extensive collection, and intricate labeling. Each step is vital to unlocking the value of data within AI applications.

In the following post, we will explore how AI is emerging not just as a technological marvel but also as a catalyst for creating millions of jobs worldwide. The critical stages of data collection and labeling, foundational processes in AI development, are not just technical necessities but also gateways to substantial global employment opportunities. 

Understanding AI

Understanding AI begins with a closer look at Generative AI, an innovative branch of artificial intelligence. Notable for its ability to create new, original data based on extensive learning from large and relevant datasets. Generative AI is pivotal in applications requiring advanced problem-solving and creativity. At the heart of this innovative branch are neural networks, sophisticated systems modeled after the human brain. These neural networks, when trained with diverse and reliable data, produce remarkable outcomes, expanding the limits of what machines can achieve through pattern recognition. 

The best AI models are deeply reliant on the quality of the data used in their training. In sectors where the stakes are high, such as autonomous driving, healthcare diagnostics, and public safety, high-quality data is non-negotiable. Therefore, the intricate process of collecting data and labeling it is absolutely crucial.

The Intricacies of Data Collection and Labeling in AI

Data Collection: The foundation of AI’s learning process

Data collection is the foundational step in the AI development process. It involves gathering vast arrays of information, from diverse environments reflective of the model’s particular objectives. The critical nature of this step lies in the need for exceptionally accurate and carefully collected information. Determining the quality and relevance of the data is a time-intensive endeavor that requires human interaction. Every company wanting to develop their own model is faced with a choice… to collect data in house or through a specialized data collection company. Being able to outsource data collection enhances both the diversity and volume of data that can be collected. Enabling a global-scale accumulation that mirrors the real world’s complexity and solves a main scaling constraint facing the Generative AI revolution.

The growth of the data collection sector is not just marked by its increasing market value, but also by the substantial creation of jobs. With the industry’s expansion and its projected growth from USD 1.66 billion in 2021 to approximately USD 8.21 billion by 2028, there’s a corresponding rise in employment opportunities in data collection and labeling​​. These jobs, which are crucial for AI and machine learning development, are becoming a stable employment opportunity for the global workforce. The sustained need for accurate data collection and labeling for an ever evolving world ensures these roles are here to stay.

With the advent of AI, the data collection industry is experiencing a significant upswing (CAGR of 25.6%). Innovators in this field, such as ArisData, are at the forefront of this acceleration. These companies focus on making data collection faster, more efficient, and reliable. 

For example, imagine a car company called Y that produces self-driving cars. Its customers suddenly report that, when using the self-driving feature, their cars are mistakenly coming to a jarring stop as if the car in front had done the same. In order for company Y to resolve the issue, it first has to parse through countless hours of videos to capture all the occurrences. In most likelihood, the more footage company Y has of coming to a jaaring stop, the better it can understand the problem and prevent it from happening again. To do so, car company Y will hire data collectors through a platform like ArisData to go through thousands of hours of self-driving footage looking for the precise event. As you can imagine, this would be a time consuming endeavor requiring the meticulous eye of hundreds if not thousands of specialized data collectors. Once the relevant snippets are collected, they can be labeled, the machine can get trained, and the self driving feature can be improved. 

Data Labeling: Giving meaning to raw data

Once data is collected, it needs to be labeled. This is where raw data is transformed into a format that AI models can interpret and learn from. Data labeling is another intricate process that involves humans classifying, categorizing, and annotating data elements to make them comprehensible to AI systems. It’s a task that demands expertise and precision, yet offers remunerated work for first time data labelers to the more experienced labeled data reviewers. 

Labeling in essence is converting unstructured data into a structured state. This conversion can take various forms, such as tagging images or videos, transcribing audio files, or categorizing text. The objective is to create a rich, well-annotated dataset that serves as the training ground for AI models. Through this dataset, AI systems can improve their performance, learning to recognize patterns and make informed decisions based on verified past experience: labeled data. Solving similar scaling challenges as collection platforms, data labeling companies like ScaleAI are hired by enterprises wanting to label their vast data sets. 

In the data labeling industry, which is experiencing substantial growth – from USD 1.82 billion in 2021 to a projected USD 9.07 billion by 2028, growing at a CAGR of 27.2%​​, companies are playing a crucial role. These firms streamline the complex process of annotating large datasets for AI development, a task essential across various sectors. By offering efficient, scalable data labeling services, they ensure high-quality training data for AI models, demonstrating their value in an industry where precision in data labeling is fundamental for the advancement of AI technologies.

Referring back to the earlier example, after the data is collected and prepared for labeling, companies such as ScaleAI deploy teams of taskers to label the videos. In this process, humans will specify where cars are in the video so that the machine can know where they are. Based on good quality labeling the AI model accurately gauges the distance of cars, thereby preventing unnecessary stops by the autonomous vehicle. This reinforcement loop is what drives our AI systems to perfection, as in the ever evolving environment of cars, other AIs environments and conditions change over time, driving the need for more data collection and labeling driving jobs into the future.

Scale AI Annotation Box https://scale.com/guides/data-labeling-annotation-guide

This is what a task looks like when finished, taskers must label what is in the image, therefore showing the AI what those “things” are. In this case, the worker labeled cars on a roundabout.

Empowering the Global South: A Symbiotic Relationship with AI

In the dynamic world of artificial intelligence, the role of human data handlers emerges as a critical element shaping the industry’s future. These data labeling, and collection platforms have humans at the core of their value creation machine. Their ability to scale their workforce directly correlates with their capacity to undertake larger, more ambitious projects. This underscores the strategic importance of human involvement in AI’s advancement. Prioritizing the acquisition and retention of such talent is of great importance and perhaps, one of the most challenging aspects of the industry. The key advantage for these companies lies in leveraging gig workers at competitive costs. As AI’s adoption spreads beyond tech-centric industries, the demand for custom AI systems and, consequently, for gig workers in data collection and labeling, is set to rise sharply. The demand, in volume, of data collectors and labelers will be tough to fulfill. Interestingly, the scaling issue is not the most complex problem data handling companies face when expanding their global pool of workers. 

A cost-competitive and diverse workforce is key for companies aiming to lead in the competitive data handling industry. The challenge lies in the fact that the gig workers fulfilling these requirements, in a cost effective and diverse way, predominantly reside in remote locations throughout the Global South. The workforce there is expanding rapidly; just in Airtm, the amount of digital entrepreneurs looking for additional earning opportunities is rising at an astounding 14% MOM.

Airtm’s community, composed of over 4 million digital entrepreneurs eager for more work opportunities, is easy and free (for now) to access by platforms offering online gigs. The truth is, data handling companies can expand into many countries efficiently and at scale in a matter of weeks. Once growth happens however, leveraging, retaining, and compensating workers in the global south, safely, is a nuanced issue. Let’s delve into the challenge of harnessing the potential of digital entrepreneurs in the global south.

A fragmented workforce results in high acquisition costs

The internet’s vastness, coupled with the low digital literacy levels in developing countries, creates a unique challenge for online workers. Many, encountering online work as a novel concept, gravitate towards a single employer, under the misconception that few such opportunities exist or out of fear of navigating the dispersed and often dubious online job market. This apprehension, rooted in unfamiliarity with digital payment systems and a reliance on cash transactions, limits their exploration of other employment avenues, with many dismissing them as potential scams. Consequently, this scenario erects substantial barriers for new companies aiming to recruit these workers. The necessity for significant marketing investment and the manual onboarding of individuals to platforms elevate acquisition costs, rendering the efforts nearly insurmountable. This context subtly underscores the need for a unified platform that not only demystifies the process for workers but also offers companies a streamlined avenue to engage a diverse and untapped workforce, hinting at our envisioned solution—a central hub where workers and companies converge.

Now that we have them, how do we pay them?

Facilitating payments to online gig workers in remote regions presents a significant hurdle for data handling companies, yet the issue is more complex than it initially appears. There are platforms  for cross-border transactions capable of reaching workers in the Global South. However, these platforms exhibit critical shortcomings. Firstly, these services are notably slow, with transactions taking 1 to 5 working days to land into a user’s bank account. At first glance, this delay may seem minor. However, the majority of gig workers in the Global South operate within day-to-day economies, relying on the immediate turnaround of earnings for daily survival. Consequently, when companies issue payments weekly, the use of these platforms can severely disrupt workers’ financial stability. The delay means that funds disbursed on Wednesday might not become accessible until the following Tuesday, compounding the problem, these platforms often impose a fixed fee of 2 to 5 dollars per withdrawal transaction. Considering many of these payments hover around the ten-dollar mark, the fees can consume approximately 30% of their earnings, in addition to their actual taxes. This not only places a disproportionate financial burden on the workers but also poses a retention challenge for employers, as the inefficiencies of conventional payment methods erode the viability of sustaining a reliable gig workforce.

Company growth relies on a stable and thriving workforce

Without a stable and trusting network of workers, companies face significant hurdles in scaling and upholding their operational standards, especially amidst high turnover rates. The most beneficial scenario for these businesses is a core group of gig workers who view their work as full-time jobs—consistently striving for excellence and improvement. The importance of a dedicated and evolving workforce is paramount, particularly considering the challenges in onboarding new taskers and guiding them to become adept data handlers. Addressing pivotal concerns such as financial stability is essential for the retention and development of talent. To further this goal, companies should actively seek ways to keep their workers engaged and supported, even in the absence of immediate tasks. This might include exploring partnerships to create continuous engagement opportunities, hinting at a proactive approach to workforce management and investment in their long-term growth and satisfaction.  

Ensuring integrity and trust in the gig economy: The role of expert partnerships

Building on the discussions of payment challenges and the importance of maintaining worker engagement, addressing fraud emerges as a critical concern in managing a global gig workforce. As companies navigate these waters, the reliance on payment partners, while instrumental, introduces complexities in ensuring data integrity and preventing fraudulent practices. The rapid completion of tasks for financial gain, often at the expense of quality, underscores the necessity for a vigilant approach to fraud. In this context, the integration of a partner well-versed in the nuances of the gig economy becomes invaluable. Such a partner would not only understand the intricacies of global payment systems but also be adept at identifying and mitigating risks associated with deceitful practices. This addition would complement existing payment solutions by offering expertise in fraud prevention, thus enhancing the overall trustworthiness and efficacy of the ecosystem. Leveraging a partner with a deep understanding of the gig economy’s challenges and dynamics can ensure higher standards of data accuracy and integrity, fostering a responsible and sustainable environment for all stakeholders.

Embracing strategic partnerships in the gig economy

The race to optimize gig economy operations presents a stark opportunity cost for companies not yet leveraging strategic partnerships. Industry frontrunners are swiftly moving to ally with platforms that offer comprehensive solutions, encapsulating everything from seamless payments to fraud detection and community engagement. Airtm’s partnership with ScaleAI exemplifies this trend, highlighting the significant advantages of integrating with a platform equipped to tackle the gig economy’s multifaceted challenges head-on.

Airtm distinguishes itself by creating a pivotal hub, directly connecting digital workers with companies in need of their skills to operate effectively or achieve growth. This not only streamlines the recruitment and payment processes but also fortifies the ecosystem against fraud, ensuring a secure and efficient partnership. In a landscape where operational agility and trust are paramount, companies standing on the sidelines of such partnerships may find themselves at a competitive disadvantage, missing out on the streamlined efficiencies and expanded workforce capabilities that platforms like Airtm provide.

Charting the Future: Airtm's Commitment to Revolutionizing AI Data Handling

In the rapidly evolving landscape of artificial intelligence and the gig economy, standing at the crossroads of innovation and human ingenuity, companies are presented with a clear mandate: to adapt or be left behind. The insights gleaned from the intricate processes of data collection and labeling, and the strategic integration of a global workforce underscore not just opportunities but imperatives for businesses aiming to thrive in this dynamic environment.

As we’ve navigated through the challenges of scaling, payment intricacies, and the critical need for fraud prevention, one solution emerges with compelling clarity: the power of strategic partnerships. The collaboration between Airtm and ScaleAI serves not merely as a beacon but as a proven path forward, demonstrating the tangible benefits of embracing platforms that encapsulate comprehensive solutions to navigate the gig economy’s complex challenges.

This narrative is not just about overcoming obstacles; it’s a call to action for industry leaders. The time to act is now, to explore and engage with partnerships that not only address the immediate needs of payment and fraud prevention but also unlock the full potential of a stable, thriving, and engaged global workforce. In doing so, companies not only safeguard their operational integrity but also position themselves at the forefront of the AI revolution, ready to harness the boundless opportunities that lie ahead.

As we at Airtm venture deeper into the realm of AI data, we invite industry leaders to join us in exploring innovative solutions and strategic partnerships. Let the journey from insight to action begin. Reach out, learn more, and take the decisive step towards future-proofing your growth in the AI-driven world. The moment to pivot towards strategic collaboration is here, and the rewards for those who dare are both immediate and immense.

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