The AI industry has reached an important turning point. A peer-reviewed study published in PNAS found that simply making language models larger delivers diminishing returns in performance, with only minimal improvements despite massive increases in size and compute power. Companies spend millions chasing technical gains like these, yet many still treat their most important differentiator, data creation, as a cost to cut rather than a quality to improve.

This problem shows up most clearly in how companies treat data annotation workers. While executives debate advanced AI models in meetings, the people actually teaching these systems to understand the world often work under conditions that would be unacceptable in any other professional job.

In this blog post, we’ll explore why AI ethics must go beyond simple compliance, examine the hidden workforce behind AI systems, show how fair work practices directly improve data quality and model performance, discuss sustainability in both human and technical terms, share Welo Data’s approach to ethical AI development, and explain why ethical workforces are essential for building stronger AI systems.

Why AI Ethics Must Go Beyond Compliance

Most companies approach AI ethics like filling out a tax form. They follow the minimum requirements from rules like the EU AI Act, check the right boxes, and consider their job done.

This approach misses the bigger picture. Following rules keeps companies out of legal problems, but it doesn’t make their AI systems better. The companies building the most effective AI understand that frameworks like UNESCO’s Ethics of AI can probably improve system reliability and performance. For example, research shows that integrating transparency, fairness, and privacy into AI ethics reduces bias and increases accountability in deployed systems

The companies that will lead the next wave of AI development are finding that ethical practices create actual competitive advantages, especially in the often-ignored area of workforce management.

The Human Workforce Powering Ethical AI

Every AI system you use exists because human workers spent hours teaching computers to recognize patterns. These people label images, transcribe audio, check model outputs, and do the detailed work that turns raw data into training sets that can produce reliable AI systems.

Despite their important role, these workers often face serious challenges. Research from Stanford shows that data workers often earn as little as $1.46 per hour after taxes. TIME’s investigation found that millions of people do this essential work without job security, benefits, or opportunities to advance their careers.

The industry has created two different approaches. Some companies treat annotation as cheap labor, focusing mainly on low costs and fast delivery. Others see data annotators as skilled professionals whose expertise directly affects how well their systems work. This basic difference in approach produces very different results.

Multiple organizations have documented the conditions of the hidden AI workforce. Research from the Oxford Internet Institute shows that clearer guidance and better pay directly improve annotation accuracy. The Fairwork AI Report highlights how fair work principles can be applied in practice. Meanwhile, Stanford HAI and the Montreal AI Ethics Institute provide broader evidence on the risks of poor labor practices.

How Fair Labor Directly Improves AI Data Quality

The connection between ethical workforce practices and better AI performance follows a clear pattern. Fair pay and stable jobs help annotators develop specialized skills. These skills lead to more accurate data labeling. More accurate labeling produces better model performance.

Research on machine learning model performance consistently shows that data quality is the main factor determining how well AI systems work. As Andrew Ng puts it, “If 80 percent of our work is data preparation, then ensuring data quality is the most critical task for a machine learning team.”

For years, many large model builders relied on “good enough” data to establish baseline capabilities. That approach worked when the goal was to prove general competence. But as AI moves into specialized, high-stakes domains like healthcare, finance, and scientific research, the margin for error has collapsed. A mislabeled record in a medical dataset or a culturally misinterpreted image in security software can cascade into serious failures. 

For example, “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI shows that small data issues in healthcare or conservation can lead to big downstream errors, because the stakes and visibility are so high. This makes the quality of the workforce behind data preparation more important than ever.

Companies using ethical workforce practices typically see several quality improvements:

• Skill Development: Stable jobs allow annotators to build knowledge in specific areas and understand complex labeling requirements. Research from companies like Aya Data shows that organizations offering long-term contracts and good training programs see clear improvements in the accuracy and consistency of their annotations.
• Keeping Good People: Fair pay and respectful treatment greatly reduce worker turnover. High turnover creates errors and inconsistencies throughout training processes, while stable employment keeps institutional knowledge and maintains consistent labeling across projects.
• Cultural and Context Accuracy: Ethical annotation practices usually involve hiring diverse workforces and respecting local expertise. This diversity improves model performance across different demographic groups and use cases while reducing the bias problems that affect many AI systems. For example, the PAIR study found that using a representative pool of annotators significantly improves calibration and fairness of models across demographic groups.

Companies that focus on cutting costs instead of investing in their workforce inevitably hurt data quality. The shortcuts and inconsistencies that come from poor working conditions get built into training data and stay there throughout model development.

Ethical AI as the Key to Sustainable Governance

Poor workforce treatment creates system-wide problems that get worse over time. Overworked annotators make more labeling mistakes. High turnover eliminates institutional knowledge just as workers develop expertise. Lack of investment in training and development means quality issues go unaddressed and spread across datasets.

Ethical workforce practices create positive cycles that strengthen AI systems over time. Research on AI governance frameworks shows that organizations with strong ethical frameworks for workforce management are more resilient and can scale their AI better.

The changing regulatory landscape increasingly reflects this understanding. The EU AI Act specifically addresses workforce implications, requiring human oversight and transparent processes for high-risk AI applications. Future regulations will likely extend these requirements, making ethical workforce practices a compliance requirement rather than just a competitive choice.

Smart organizations recognize that workforce fairness and system reliability work together rather than against each other. Studies from institutions like the Montreal AI Ethics Institute emphasize that sustainable AI development must consider the working conditions and career development of data annotators alongside technical performance.

How Welo Data Builds Ethical AI with Fair Work

At Welo Data, fair labor isn’t an afterthought; it’s the backbone of our quality infrastructure. We’ve built our systems on the belief that ethical workforce practices are not only a moral obligation, but also the foundation of high-performing, trustworthy AI.

Transparent Pay & Workforce Well-Being

We provide competitive, clearly defined pay structures that recognize the skill and impact of annotation work. Just as importantly, we invest in whole-person support: preventive health resources, global wellness programs, and confidential counseling are available to contributors. This ensures that the people building AI are equipped to professionally and personally thrive.

Identity, Integrity & Trust at Scale

Through our proprietary NIMO platform, we continuously verify workforce identity and qualifications—blocking fraud, preventing misrepresentation, and safeguarding data integrity. In 2024 alone, NIMO processed over 48,000 identity checks with a 98.7% match rate, monitored 1.2M+ task events monthly, and prevented thousands of fraudulent access attempts. This means our clients can trust not just the data, but the people behind it.

Professional Development as a System

Annotation isn’t gig work at Welo—it’s a career path. Using our Talent Platform, we integrate skills testing, e-learning, certifications, and structured advancement into every contributor’s journey. Annotators progress into roles such as QA leads, domain specialists, and project managers, supported by continuous coaching and recertification programs

Cultural & Domain Expertise Built In

We don’t treat contributors as interchangeable. Our domain-aligned recruitment model ensures that experts in healthcare, law, finance, and other high-stakes fields are carefully sourced, screened, and retained. Combined with culturally fluent raters across 200+ languages, this approach improves both model accuracy and fairness across diverse populations.

Proven Business Outcomes

The result of this investment is measurable. Client satisfaction scores reflect higher trust, and project timelines accelerate due to lower turnover and stronger workforce stability. Ethical labor practices are not extra costs; they are competitive advantages that compound over time.

Ethical Workforces Build Stronger AI Systems

Evidence from multiple research areas confirms a basic principle: sustainable, high-performing AI systems cannot be built on exploited labor. One example is our partnership with a global tech company, where trained human evaluators helped an LLM “think like a journalist.” The result was a more accurate, scalable, and trustworthy AI system built on ethical workforce practices.

Data quality research consistently shows that ethically-sourced training data produces more accurate, reliable, and fair AI systems. Workforce treatment studies show that exploitative practices create exactly the types of instability and errors that undermine both system trust and ability to scale.

Organizations serious about AI performance must look at their entire development process, not just their final products. This review should include pay practices, working conditions, career development opportunities, and recognition of the cultural and domain expertise that data workers bring to system development.

The AI industry faces a basic strategic choice. Companies can continue the cost-cutting approaches that characterize much current practice, or they can invest in the human infrastructure that enables truly superior AI systems. The organizations that recognize and act on the connection between workforce treatment and system quality will set the performance standards for the next generation of AI development.

Ethical workforce practices create stronger, more reliable AI systems. At Welo Data, we’ve put this into action; discover how in our success stories.

Frequently Asked Questions (FAQs)

What specific improvements in data quality can companies expect from ethical workforce practices?

Organizations using ethical workforce practices typically see improvements in annotation accuracy, reductions in workforce turnover, and much better consistency across datasets. Research from ethical annotation providers confirms these ranges while noting that improvements often get better over time as workforce expertise develops.

How do regulatory frameworks like the EU AI Act address workforce ethics in AI development?

The EU AI Act establishes human oversight requirements and transparency obligations for high-risk AI applications. While it doesn’t explicitly require specific workforce treatment standards, these frameworks create accountability structures that encourage better labor practices throughout the AI development process. Future regulations will likely address workforce considerations more directly.

What’s the cost difference between ethical and exploitative data annotation practices?

Ethical practices require higher upfront investment in wages, training, and infrastructure. However, they typically reduce total project costs over the complete project timeline through lower turnover, fewer quality issues, less revision work, and more efficient project completion. Organizations should look at annotation costs across entire project timelines rather than focusing only on hourly rates.

How can companies measure the ROI of investing in ethical workforce practices for AI development?

Key metrics include annotation accuracy rates, workforce retention percentages, project completion timeframes, model performance metrics, client satisfaction scores, and regulatory compliance costs. Organizations should also track quality assurance overhead, revision requirements, and time-to-deployment metrics, which typically improve significantly with ethical workforce practices.

What are the main risks of continuing exploitative labor practices in AI data annotation?

Exploitative practices create multiple business risks, including inconsistent training data quality, high workforce turnover costs, increased quality assurance overhead, regulatory compliance problems, reputation damage, and ultimately poor-performing AI systems that fail to meet performance goals. These risks often get worse over time and become increasingly expensive to fix.

How do cultural competency and workforce diversity improve AI model performance?

Diverse annotation teams bring different perspectives and cultural knowledge that help identify edge cases, reduce training data bias, and improve model performance across different demographic groups. Research shows this is particularly important for AI systems used globally or across diverse user populations, where cultural context significantly affects how well systems work.

What training and career development programs should organizations provide to data annotators?

Effective programs include domain-specific training, quality assessment methods, project management skills, and clear advancement paths to roles in quality assurance, data analysis, and AI system development. Organizations investing in comprehensive development programs report improved data quality, higher workforce satisfaction, and reduced recruitment costs.

How can organizations begin implementing more ethical AI workforce practices?

Organizations should start with comprehensive reviews of current annotation practices, including pay analysis, turnover rate assessment, and quality metric evaluation. Implementation steps include establishing competitive pay scales, creating stable employment structures, developing training programs, and building clear career advancement paths. Direct engagement with annotation workforces often reveals specific improvement opportunities and implementation priorities.

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