Collective dynamics of strategic classification
Marta C. Couto, Flavia Barsotti, Fernando P. Santos

TL;DR
This paper uses evolutionary game theory to analyze how strategic users and adaptive algorithms interact in high-stakes AI decision-making, revealing complex feedback loops and potential mitigation strategies.
Contribution
It introduces a rigorous framework for studying the feedback dynamics between strategic users and institutions, including interventions like detection and recourse to improve outcomes.
Findings
Improved detection reduces social costs and encourages user improvement.
Algorithmic recourse can steer dynamics towards better user performance.
Re-adaptation speed influences the final strategic equilibrium.
Abstract
Classification algorithms based on Artificial Intelligence (AI) are nowadays applied in high-stakes decisions in finance, healthcare, criminal justice, or education. Individuals can strategically adapt to the information gathered about classifiers, which in turn may require algorithms to be re-trained. Which collective dynamics will result from users' adaptation and algorithms' retraining? We apply evolutionary game theory to address this question. Our framework provides a mathematically rigorous way of treating the problem of feedback loops between collectives of users and institutions, allowing to test interventions to mitigate the adverse effects of strategic adaptation. As a case study, we consider institutions deploying algorithms for credit lending. We consider several scenarios, each representing different interaction paradigms. When algorithms are not robust against strategic…
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