AI Confidence Scores vs Research Confidence: Why They’re Not the Same Thing

By Philip Burgess | UX Research Leader

When I first encountered AI confidence scores, I assumed they worked just like the confidence levels I learned about in research studies. It seemed natural to think that a high AI confidence score meant the same thing as a high confidence interval in scientific research. Over time, I realized this assumption was misleading. The two concepts serve different purposes and come from very different processes. Understanding these differences can help us better interpret AI outputs and avoid common misunderstandings.

Close-up of AI confidence scores on a computer screen

What AI Confidence Scores Actually Represent

AI confidence scores are numbers generated by machine learning models to indicate how likely the model thinks its prediction is correct. For example, if an AI model classifies an image as a cat with a confidence score of 0.85, it means the model estimates an 85% chance that the image is a cat based on its training data.

These scores come from the model’s internal calculations, often based on probabilities derived from patterns in the data it has seen. The score reflects the model’s certainty about its own prediction, but it does not guarantee accuracy in the real world.

Key Characteristics of AI Confidence Scores

• Model-dependent: Scores depend on how the AI was trained, the data quality, and the algorithm used.

• Relative certainty: They show how confident the model is compared to other possible outputs.

• Not statistical confidence: They do not represent a statistical confidence interval or margin of error.

• Can be misleading: High confidence does not always mean the prediction is correct, especially if the model encounters unfamiliar data.

  
  

How Research Confidence Works

In research, confidence usually refers to statistical confidence intervals or confidence levels. These are calculated using well-established mathematical formulas based on sample data. A 95% confidence interval means that if the same study were repeated many times, 95% of the calculated intervals would contain the true population parameter.

This type of confidence is about the reliability of an estimate derived from data, not about the certainty of a single prediction. It provides a measure of uncertainty around a result, helping researchers understand how much trust to place in their findings.

Key Characteristics of Research Confidence

• Based on statistical theory: Uses probability distributions and sample sizes.

• Quantifies uncertainty: Shows the range within which the true value likely falls.

• Reproducible: Can be recalculated with new data or repeated studies.

• Supports decision-making: Helps determine if results are significant or due to chance.

  
  

Why Confusing the Two Can Cause Problems

I once worked on a project where an AI tool flagged medical images with confidence scores. The team assumed a 90% AI confidence score meant the diagnosis was 90% likely to be correct. This led to overreliance on the AI and missed some critical errors.

The problem was that the AI confidence score did not account for real-world variability, data biases, or the possibility of rare conditions. Unlike a research confidence interval, the AI score was not a statistically validated measure of uncertainty.

This confusion can lead to:

• Overtrust in AI predictions: Believing the AI is more accurate than it actually is.

• Misinterpretation of results: Treating AI scores as definitive proof rather than estimates.

• Poor decision-making: Ignoring the need for human judgment or further validation.

  
  

Practical Tips for Using AI Confidence Scores Wisely

To avoid these pitfalls, I recommend the following:

• Understand the AI model’s limitations: Know how it was trained and where it might fail.

• Use confidence scores as guides, not guarantees: Treat them as one piece of information among many.

• Combine AI outputs with expert review: Human insight can catch errors AI might miss.

• Look for calibration: Some AI models provide calibrated confidence scores that better reflect real-world accuracy.

• Consider the context: In high-stakes fields like healthcare, even high confidence scores require careful validation.

  
  

Researcher comparing AI confidence scores with statistical confidence intervals

Bridging the Gap Between AI and Research Confidence

The gap between AI confidence scores and research confidence can be narrowed by improving transparency and education. AI developers can:

• Provide clear explanations of what confidence scores mean.

• Offer tools to assess model calibration and reliability.

• Encourage users to interpret scores with caution and in context.

  
  

Researchers and practitioners can:

• Learn the differences between AI and statistical confidence.

• Use AI as a support tool rather than a sole decision-maker.

• Advocate for standards in reporting AI confidence metrics.

  
  

By recognizing that AI confidence scores and research confidence serve different roles, we can better harness AI’s potential while maintaining rigorous standards for accuracy and trust.