识别影响未来预测的行为：数据流中的表现性概念漂移检测

概念漂移在 Stream Learning的背景下已广泛研究。然而，往往假设部署的模型的 predictions在 system所经历的概念漂移中扮演 no role的角色。更深入的检查揭示了这种情况并非总是如此。自动交易可能容易受到 self-fulfilling feedback loops的影响。同样，恶意实体可能适应以 evading detectors from results in a self-negating feedback loop that requires the deployed models to constantly retrain。这种情况下，模型可能诱导概念漂移的 setting称为 performative。In this work, we investigate this phenomenon. Our contributions are as follows: First, we define performative drift within a stream learning setting and distinguish it from other causes of drift. We introduce a novel type of drift detection task, aimed at identifying potential performative concept drift in data streams. We propose a first such performative drift detection approach, called CheckerBoard Performative Drift Detection (CB-PDD). We apply CB-PDD to both synthetic and semi-synthetic datasets that exhibit varying degrees of self-fulfilling feedback loops. Results are positive with CB-PDD showing high efficacy, low false detection rates, resilience to intrinsic drift, comparability to other drift detection techniques, and an ability to effectively detect performative drift in semi-synthetic datasets. Secondly, we highlight the role intrinsic (traditional) drift plays in obfuscating performative drift and discuss the implications of these findings as well as the limitations of CB-PDD。