When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning

TL;DR

This paper reviews the concept of performative prediction in machine learning, highlighting how model deployment can influence outcomes and cause societal risks, and introduces a framework to assess and mitigate these effects.

Contribution

It provides a comprehensive synthesis of performative prediction literature and introduces the 'Performative Strength vs. Impact Matrix' for practical risk assessment.

Findings

Identifies key mechanisms of performativity in ML models.

Classifies risks associated with performative predictions.

Proposes a practical assessment framework for practitioners.

Abstract

Machine learning models are increasingly used in high-stakes domains where their predictions can actively shape the environments in which they operate, a phenomenon known as performative prediction. This dynamic, in which the deployment of the model influences the very outcome it seeks to predict, can lead to unintended consequences, including feedback loops, performance issues, and significant societal risks. While the literature in the field has grown rapidly in recent years, a socio-technical synthesis that systemises the phenomenon concepts and provides practical guidance has been lacking. This Systematisation of Knowledge (SoK) addresses this gap by providing a comprehensive review of the literature on performative predictions. We provide an overview of the primary mechanisms through which performativity manifests, present a typology of associated risks, and survey the proposed solutions offered in the literature. Our primary contribution is the ``Performative Strength vs. Impact Matrix" assessment framework. This practical tool is designed to help practitioners assess the potential influence and severity of performativity on their deployed predictive models and select the appropriate level of algorithmic or human intervention.