Superhuman Data: How Data Quality Drives AI Reliability

Superhuman Data: How Data Quality Drives AI Reliability

Accuracy is the issue; quality is the key.

AI makes things faster. No one doubts that. The issue is, does AI make things better? The number one challenge with AI right now is accuracy. Sometimes, an LLM will confidently give you an answer that is factually completely incorrect. We have all experienced this. Everyone has corrected their AI tools with knowledge their brain knows to be true, even when the model got it wrong. This is all good when you’re asking a question about something casual, but what about when a business is relying on the accuracy of AI answers for critical outputs that influence safety or life-and-death situations?

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This is the biggest challenge with relying on technology. Sure, it’s faster and less expensive, but is it accurate enough to be safe? It’s no different with self-driving autonomous vehicles or drones. Can we really trust technology to preserve human safety without humans in the loop? The answer is “not yet.”

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So what’s the leading issue organizations have today when deploying AI in production to speed up their workflow and reduce costs? The issue is accuracy. The issue is the quality of the data used to train the model, which is the primary driver of its accuracy.

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That’s the struggle. That’s the issue. And that’s what Centaur.ai was created to solve.

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Why teams get false confidence

It’s now typical for companies to deploy AI models to handle routine tasks. When training these models, these companies are hoping to produce the most accurate answers; however, very few of the models have healthy, productive, or unsuccessful ways to handle things like cases or expert disagreement. For this reason, expert disagreement can sometimes be collapsed and overlooked, which decreases the input data quality and therefore the outgoing model accuracy. If this is not something the company is aware of and intentionally working to handle in advance, the company will have excessive confidence in the accuracy of its AI output compared with what’s happening in the real world.

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How companies try to solve this today

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Companies are trying to deal with this in several ways. There are four primary approaches, and most companies use one or more of them to address it. Here are the approaches.

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Many companies try to leverage in-house subject matter experts. This is labor-intensive, expensive, and time-consuming, but it can generally produce high-quality labels. It’s simply not scalable, and it becomes unmanageable from a cost and ROI perspective, pulling these experts away from their primary job functions.

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Some companies turn to external labeling vendors with a traditional approach. Still, the quality of the data that they send back can be wildly inconsistent and challenging for the company that purchased it to audit in advance, before it enters their model and causes issues in production.

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What happens when accuracy breaks?

Many companies turned to model-assisted or automated labeling, which definitely speeds things up, but it doesn’t solve the issue of having no human brain to double-check whether or not this is accurate data that the models are being trained on

Once your model has been trained on inaccurate data, you find yourself with lower accuracy in production, which is, at best, expensive and, at worst, risky, particularly if you’re operating within a regulated environment.

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What high-stakes AI requires

High-stakes AI refers to AI models whose outputs affect a human being's safety or health. For high-stakes AI, it’s crucial to have the following:

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1) There must be a way to measure data quality, and that measurement must be scalable and repeatable.

2) When the system encounters expert disagreement, it should be used as a signal that something is wrong, rather than suppressed or hidden.

3) The evaluation steps need to reflect the real deployment conditions.

4) There needs to be the ability to audit how the labels were created

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A high-stakes data system should not treat trust as an aspirational value; it should treat it as an operational requirement.

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Centaur's Core Thesis

Centaur.ai was founded on the idea that the most accurate, highest-quality models are built not solely on human data but on a combination of human input and technological optimization.

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Centaur doesn’t hide disagreement, and it doesn’t hide variability; instead, we use these as signals to drive toward an accurate conclusion.

Centaur relies on what we call “superhuman data,” where humans and technology are strategically designed to outperform the results that either can have on their own

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How Centaur Produces Higher-Quality Data

Centaur can produce higher-quality data by doing the following:
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We work with our customers to identify a series of what we call gold standard labels. These are labels that the customer agrees correctly reflect what they are labeling. We then sneak these gold standards into the data sets that require labeling.

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We then measure each annotator's performance and check whether they accurately matched the gold standards. For each labeler, we wait for the validity of their annotations on the new items based on the accuracy of their annotations on the known gold standard answers, so someone who gets all the gold standard answers correct will have their other answers weighted higher than someone who misses gold standard answers, because we are building trust in the individual accuracy to identify the correct answer.

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This internally consistent, repeatable, wait-and-tune approach allows us to deliver results that leverage human intuition while keeping the best parts of technology.

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We can produce higher-quality outputs than any other methodology by leveraging the Center Network of experts and the Centar system and processes, which we refer to as our quality system.

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Our entire system is designed to maximize accuracy, building enough trust in the data to move quickly.

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Flexible data and labeling models

Some of our customers have data of their own that they want us to evaluate. Some of our customers looked to us to obtain the data assets for them. Whether or not you supply the data or we get it for you, we can run the data set through our quality system to ensure that your annotations are at the highest level of accuracy currently available anywhere in the world. Some companies want to provide their own annotators, and others turn to us to source labelers from our vast network, whether we supply the labelers or they provide their own. Our quality measurement system assesses the accuracy of each labeler against the gold standards. It weighs them accordingly, ensuring the highest-quality, most accurate data is fed into your model's training.

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We are your pre-deployment trust layer

Even before data ever reaches your model center, Centaur serves as a trust layer. We offer built-in data de-identification. As a result, the data we provide is created to pass security, legal, and compliance reviews.  We establish trust before deployment, not after incidents take place.

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There are three places where Centaur typically enters your workflow.

1) When you want to create a trusted evaluation baseline before a large model training investment

2) When you want to provide expert, grounded labels for complex cases and failure modes during model iteration

3) When you are stress-testing your on rare, ambiguous, or operationally significant scenarios during validation.

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So do you trust your data?

Does it take you a long time and a lot of cycles to produce a data set that you truly trust?

Do you have data that’s currently blocked by privacy, security, or procurement issues?

Do you have experts today who disagree on annotations?

Are there failures you’re experiencing that are only showing up after deployment?
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We want to run a small pilot with you. We want to take on one of your real tasks: finding a rubric, creating gold standards, and showing you the clear ROI of leveraging Centaur data.

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For a demonstration of how Centaur can facilitate your AI model training and evaluation with greater accuracy, scalability, and value, click here: https://centaur.ai/demo

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