Machine learning models are everywhere: banking, healthcare, autonomous vehicles, and countless other applications. However, like any computer program, models can have errors. The process of finding those bugs is quite different from traditional software development.
Instead of lines of code written by people, deep learning neural networks have millions of weights linked into incomprehensible networks. How do we find bugs in these systems? One powerful approach is to explain model predictions. Explainable AI reveals problems that would otherwise remain invisible.
Data Leakage: The Perfect Prediction Problem
Most ML models are supervised. You choose a prediction target, gather a dataset with features, and label each example. Then you train a model to use features to predict the target.
Surprisingly often, datasets contain features that relate to the prediction target but are not useful for actual prediction. They might be created after the event or otherwise unavailable at prediction time.
A Lending Example
Consider a lending dataset for predicting loan default. The prediction target is whether the loan status is "Fully Paid" or "Charged Off" (defaulted). The dataset includes fields like total payments received and loan amount.
Notice the pattern: whenever a loan defaults, total payments are less than the loan amount. This is nearly the definition of default. By the end of the loan term, the borrower paid less than what was loaned.
Including total payments gives nearly perfect information. But we do not have total payments until after the entire loan term, often three years later. Including both loan amount and total payments in training data is data leakage of the prediction target.
A model built on this data will perform very well. Too well. If we run a feature importance algorithm, we will see these variables come up as highly important. With any luck, we realize this is data leakage.
Detecting Leakage Through Explanations
When a feature appears disproportionately important in explanations, especially for a model with suspiciously high accuracy, investigate why. Is that feature available at prediction time? Is it causally related to the outcome or merely a consequence of it?
There can be more subtle forms of leakage too. A grade assigned by a proprietary scoring model might itself use features you are trying to predict. A FICO score might encode information from the same data you are modeling. Any predictive data that you cannot or will not use for actual prediction is data leakage.
Data Bias: The Spurious Correlation Problem
Suppose through poor data collection or a bug in preprocessing, your data contains bias. More specifically, there is a spurious correlation between a feature and the prediction target. Explaining predictions will show an unexpected feature often being important.
Simulating Data Bias
We can simulate a data processing bug by dropping all defaulted loans from zip codes starting with certain digits. Before this bug, zip code is not very predictive of default. After this bug, any zip code starting with those digits will never show defaults. Zip code will show up as highly important for predicting no default.
If we investigate by examining predictions where zip code was important, we might notice the pattern and realize the bias. The feature importance explanation reveals a problem that accuracy metrics alone would not.
Implications for Production Models
A model built from biased data is not useful for making predictions on unbiased data. It is only accurate within the biased dataset. This model is fundamentally buggy, even if test metrics look good.
Bias can enter data through many paths: selective sampling, labeling errors, preprocessing bugs, or historical discrimination encoded in training data. Explanations help surface these problems by revealing unexpected patterns of feature importance.
Using Explanations for Debugging
If you are not sure your model is using data appropriately, use feature importance explanations to examine its behavior.
Look for unexpectedly important features. If a feature shows high importance but should not be predictive based on domain knowledge, investigate why.
Check for data leakage patterns. Features that are consequences of the prediction target rather than causes will show unusually high importance.
Examine importance across different data slices. If a feature is important for some subgroups but not others, there may be bias in how data was collected or labeled for those groups.
Compare training and production explanations. If feature importance patterns differ significantly between training evaluation and production, this suggests distribution shift or data quality issues.
Beyond Explanations
Other model debugging methods exist that do not involve explanations. Looking for overfitting or underfitting based on model architecture. Regression tests on a golden set of predictions you understand. Monitoring performance metrics over time in production.
Explanations complement these approaches by providing insight into why models behave as they do, not just whether they are accurate. When traditional metrics suggest a problem but do not reveal its source, explanations often point the way.
Building Debug-Ready Models
The organizations that successfully deploy ML at scale build debugging capabilities into their MLOps processes from the start.
Explainability infrastructure should be part of the model development workflow. Every model should be explainable before it reaches production. When problems arise, teams should have tools ready to investigate.
Model monitoring should track not just accuracy but also explanation patterns. Changes in feature importance over time can signal drift or emerging bias before accuracy metrics degrade.
Finding bugs in ML models is different from debugging traditional software. The bugs live in data as much as in code. Explainable AI gives us the visibility we need to find and fix them.
