Why Simple Fixes for Missing Data Can Create Big Problems in AI

When building AI systems, missing data is unavoidable. Maybe patients didn’t report their income, maybe students skipped a survey, maybe a sensor failed. To keep things moving, developers often use quick fixes like mean imputation, replacing missing values with the average of what’s there.

It sounds harmless. But in practice, it can quietly introduce bias, reduce accuracy, and create unfair outcomes.

What is imputation?

Imputation is the process of filling in missing values in your dataset. Common methods include:

• Mean/median imputation: Replace missing values with the column average.

• Mode imputation: Replace with the most common category.

• Regression or machine learning-based imputation: Predict missing values based on other features.

• Multiple imputation: Use repeated sampling to better reflect uncertainty.

Why basic methods are risky

The catch is that most missing data isn’t random. For example:

• People with lower incomes may be less likely to report income.

• Women may be more likely to skip certain health questions.

• Certain age groups may avoid disclosing sensitive details.

If you plug in an overall average, for example, you’re not just filling a gap, you’re potentially overwriting group-specific patterns. This can:

• Misrepresent one group’s data.

• Reduce overall model accuracy.

• Create unfair outcomes for underrepresented groups.

A real-world example

A healthcare team building a risk prediction model found that income data was often missing. They used mean imputation to fill the gaps.

But missingness wasn’t evenly distributed. Women reported income less often, and the average skewed higher due to men’s higher reported earnings. The imputation overstated women’s incomes, which in turn caused the model to underestimate women’s health risks.

The result: some women at genuine risk were flagged as low priority for follow-up care—showing how a simple technical shortcut can lead to real-world harm.

What regulators say

The EU AI Act requires high-risk AI systems to use datasets that are “complete and free from errors, insofar as possible.” That includes dealing with missing data responsibly. The GDPR also requires data to be accurate and fair, which risky imputations can undermine.

In short: regulators expect you to treat imputation as a serious decision, not a background preprocessing step.

How CleanAI helps

CleanAI has built-in support for identifying risky imputation practices in the coding environment. For example:

• Flagging imputation actions

• Issuing warnings 

• Suggesting alternatives 

• Documenting choices 

This prevents risky imputations from slipping through unnoticed and ensures developers confront the trade-offs.

Quick fixes like mean imputation may save time, but they can quietly undermine fairness, accuracy, and compliance. Treating imputation as a governance issue rather than just a coding detail, helps ensure your AI is both trustworthy and lawful.

Tools like CleanAI make this easier by flagging risky shortcuts, guiding safer alternatives, and recording decisions transparently.

Emma Kitcher, Privacy Nerd and Founder of CleanAI