Model Transferability With Responsive Decision Subjects
This addresses the challenge of maintaining model accuracy in interactive environments where human agents respond strategically, which is incremental in formalizing transferability under such conditions.
The paper tackles the problem of whether an accurate algorithmic predictor remains effective when human decision subjects strategically adapt to it, formalizing model transferability under induced distribution shifts. It provides upper bounds for performance gaps and lower bounds for trade-offs between source and induced target distributions, with analysis for covariate and target shift settings.
Given an algorithmic predictor that is accurate on some source population consisting of strategic human decision subjects, will it remain accurate if the population respond to it? In our setting, an agent or a user corresponds to a sample $(X,Y)$ drawn from a distribution $\cal{D}$ and will face a model $h$ and its classification result $h(X)$. Agents can modify $X$ to adapt to $h$, which will incur a distribution shift on $(X,Y)$. Our formulation is motivated by applications where the deployed machine learning models are subjected to human agents, and will ultimately face responsive and interactive data distributions. We formalize the discussions of the transferability of a model by studying how the performance of the model trained on the available source distribution (data) would translate to the performance on its induced domain. We provide both upper bounds for the performance gap due to the induced domain shift, as well as lower bounds for the trade-offs that a classifier has to suffer on either the source training distribution or the induced target distribution. We provide further instantiated analysis for two popular domain adaptation settings, including covariate shift and target shift.