Play to Your Strengths: How Partnerships Drive AI Data Quality

In the AI world it is no secret that the quality of the data used to train models is critical. Not just any data will do—AI systems need high-quality, precisely labeled human data. There is an adage in the AI industry “garbage in, garbage out” that keenly depicts the need for high quality data . As AI becomes more integrated into our daily lives, the importance of ensuring that the data feeding these systems is top-notch cannot be overstated.

Take the Google AI Helper crisis for example – Google used Reddit data to create search engine summaries which led to somewhat hilarious results, but in some cases this data could pose a true safety issue. But the problem is, once poor data is fed into a model, it can be difficult or impossible to “untrain” the model – you may end up damaging it beyond repair.

So how do you ensure that your AI is learning from the very best data?

• Stringent Quality Control: Human data labeling is a meticulous process that demands attention to detail. Implementing multiple layers of quality checks—combining both automated tools and human reviewers—helps ensure that each piece of data is labeled accurately and consistently.
• Mission-Driven Workforce: The people behind the data labeling process are crucial. They need to be empowered, well-trained, and fully bought into the mission. It’s essential for labelers to deeply understand the end goal of the data they’re working with. When the workforce is aligned with the bigger picture, they’re more likely to produce data that truly meets the needs of the AI models being developed.
• Balancing Scale and Precision: As AI projects grow, the need for more labeled data increases. It’s important to work with a partner who can scale operations while maintaining the same high level of precision. This is where specialized partners with operational expertise come into play.

This is all easier said than done. AI companies have teams of researchers and engineers working in the background to improve AI models. Program Managers and Engineers at AI companies can have daunting workloads.

Why a Specialized Partner Makes All the Difference

You may think that because data quality is of utmost importance, it’d be a poor choice to outsource it. However, collaborating with a partner who specializes in data labeling and operational management allows AI companies to focus on what they do best—developing cutting-edge technology. Meanwhile, the partner ensures that the data labeling process is handled efficiently and with the utmost care. Here’s why this approach works:

• Operational Mastery: A specialized partner has honed their processes over years of managing large-scale projects, ensuring that every step of the data labeling process is optimized for accuracy and efficiency.
• Protecting Program Manager Bandwidth: Working with an expert partner can lead to significant savings in PM bandwidth, and ultimately reduce costs in hiring a managerial team for a data labeling workforce. Training human capital is a large time investment, and a strategic partner allows AI companies to allocate resources where they matter most—innovation and development.
• Access to Skilled Talent: A well-connected partner has access to a global network of skilled workers, ensuring that your data labeling team is always capable, well-trained, and ready to meet the demands of your project.

For AI companies striving to develop models that are both innovative and reliable, partnering with the right specialized expert for human data is an excellent strategic choice.