The changing circumstances of data labeling and the shift away from independent contractor labor
Generative AI data labeling started as a straightforward task, which could be easily handled by independent contractors. However, with the advancement of AI models, particularly Large Language Models (LLMs), the nature of this work has evolved significantly.
The growing complexity and sophistication of data labeling tasks have highlighted the need for a more specialized, committed workforce, and to that extent, W2 contract employees are becoming a stronger option for many companies.
Why does it make sense for companies to transiton to W2 employees, especially when it comes to data labeling?
There are a surprising number of benefits to staffing a data labeling team with W2 employees, but here's a quick overview to start:
Like many jobs in artificial intelligence, the intricacy of modern data labeling demands extensive training to ensure the production of high-quality AI training data. W2 contract employees can receive direct, hands-on training and guidance to stay aligned with company priorities.
This is a unique advantage over independent contractors, who must observe regulatory prohibitions against receiving training from clients as part of appropriate worker classification.
For example, companies implementing advanced labeling workflows for niche domains can train W2 employees to specialize in these areas. This is especially important in industries like healthcare, finance, and autonomous vehicles.
Trained employees can provide more consistent, accurate results that meet both technical and compliance requirements.
As AI companies rely more on specialists with advanced skill sets and deep domain expertise, the recruitment and retention of these high-caliber professionals have become very important. These roles are demanding and often require a higher investment in talent acquisition.
In general, W2 contract employment results in better retention rates compared to independent contractors. This stability allows companies to maintain a skilled workforce over time, which has a cascading effect.
W2 contract employees typically work exclusively for their employer, use company-provided equipment, operate onsite, and sign non-disclosure agreements. These are all great practices for reducing the risk of data breaches and ensuring better compliance with data security and privacy standards.
By contrast, independent contractors serve multiple clients, use their own equipment, and have more autonomy over their work environment.
While this isn’t a guaranteed formula for security issues, it can result in exposure in many ways that W2 contract employment will never encounter.
Employee engagement is key to productivity and accuracy in data labeling tasks. Engaged employees who receive regular feedback tend to perform better. W2 contract employees can be more closely managed and mentored, fostering an environment of continuous improvement.
The same is unfortunately not possible for independent contractors, who, as stated previously, operate independently and are subjected to regulatory prohibitions against management and training from their clients.
With W2 contract employees, there is more precise control over scheduling and task management. This is particularly advantageous for data labeling tasks that require quick adjustments and priority shifts.
While independent contractors set their own schedules, the ability to direct W2 employees more accurately ensures that projects remain on track and can adapt swiftly to changing requirements.
As the needs of AI data labeling continue to advance, so too does the need for highly trained, reliable, and engaged workers. W2 contract employees provide numerous advantages over independent contractors, including direct training, higher retention rates, enhanced data security, improved performance management, and superior time management.
For companies aiming to produce top-tier AI training data, the shift towards W2 contract employment is not just a trend but a strategic necessity.
Independent contractors, while useful in some scenarios, present challenges for companies focused on scalability and precision.
These challenges include:
One of the most common horror stories, when it comes to contract employment, is the risk of misclassification. This is far from a little employment "oopsie" as misclassification can lead to significant legal and financial consequences, including penalties and back taxes owed.
This distinction is particularly important in the AI industry, where accurate worker classification minimizes risks and supports long-term growth.
The shift towards W2 contract employment is not just a trend—and the repercussions from misclassification are very real. To produce top-tier AI training data, companies should leverage the advantages of W2 employees.
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