The term model drift originates from the fields of artificial intelligence and big data, and describes an important phenomenon in the use of learning computer models. Model drift happens when the quality of predictions made by an AI model deteriorates over time because the data or conditions with which the model works change.
A simple example: An online shop uses artificial intelligence to predict which products customers are likely to buy next. If purchasing behaviour shifts due to a trend or societal change, the original model suddenly starts working with „old“ assumptions. The recommendations become less accurate – the model has, so to speak, „drifted“ from its original state.
Model drift is therefore important to monitor because it shows decision-makers that artificial intelligence is not a self-fulfilling prophecy. Models must be regularly reviewed and, if necessary, retrained so that they continue to deliver reliably usable results. This ensures that the technology remains a useful tool in everyday work and effectively supports data-based decisions.
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