Imagine you have developed a machine learning model to predict loan defaults for a bank. You train and validate your model using historical data collected over several years, during which the economy was stable and lending practices were consistent. Your model performs exceptionally well in testing, so you confidently deploy it to support real loan decisions. However, shortly after deployment, a sudden economic downturn causes a significant shift in applicant profiles and their likelihood of default. New applicants are more likely to have unstable employment or different financial backgrounds than those in your training data. Because your evaluation process did not account for this potential distribution shift — specifically, a covariate shift in applicant characteristics — your model's performance in the real world drops sharply. Loans are approved for riskier applicants, leading to higher default rates and financial losses for the bank. This scenario highlights how ignoring distribution shift can result in models that are ill-equipped for the realities they will face, despite appearing successful during traditional evaluation.

Key evaluation failures emerge when distribution shift is not properly addressed:

• Models may demonstrate high accuracy during testing but fail to generalize to new data drawn from a shifted distribution;
• Important risks and vulnerabilities remain hidden, leading to costly errors or unsafe decisions in deployment;
• Stakeholders place unwarranted trust in performance metrics that do not reflect real-world challenges;
• Post-deployment monitoring and adaptation are neglected, making it difficult to detect or respond to changing data patterns;
• Regulatory and ethical issues may arise if models produce biased or unfair outcomes due to unrecognized shifts in population or context.