Concept Drift has been extensively studied within the context of Stream Learning. However, it is often assumed that the deployed model's predictions play no role in the concept drift the system experiences. Closer inspection reveals that this is not always the case. Automated trading might be prone to self-fulfilling feedback loops. Likewise, malicious entities might adapt to evade detectors in the adversarial setting resulting in a self-negating feedback loop that requires the deployed models to constantly retrain. Such settings where a model may induce concept drift are called 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.

翻译：在流式学习背景下，概念漂移已得到广泛研究。然而，研究通常假设已部署模型的预测对系统所经历的概念漂移不产生影响。深入分析表明，实际情况并非总是如此。自动化交易可能容易陷入自我实现的反馈循环；同样，在对抗性环境中，恶意实体可能通过自适应来规避检测器，从而形成自我否定的反馈循环，迫使已部署模型持续重新训练。这类模型可能诱发概念漂移的场景被称为执行性场景。本研究针对该现象展开探索，主要贡献如下：首先，我们在流式学习框架中定义执行性漂移，并将其与其他类型的漂移进行区分。我们提出一种新型漂移检测任务，旨在识别数据流中潜在的执行性概念漂移，并首次提出名为CheckerBoard执行性漂移检测（CB-PDD）的检测方法。我们将CB-PDD应用于具有不同程度自我实现反馈循环的合成与半合成数据集。实验结果表明CB-PDD具有高效性、低误检率、对内在漂移的鲁棒性、与其他漂移检测技术的可比性，以及在半合成数据集中有效检测执行性漂移的能力。其次，我们揭示了内在（传统）漂移对执行性漂移的混淆作用，并讨论了这些发现的意义以及CB-PDD的局限性。