Revisiting Dynamics in Strategic ML

Strategic classification concerns the problem of training a classifier that will ultimately observe data generated according to strategic agents’ responses. The commonly adopted setting is that the agents are fully rational and can best respond to a classifier, and the classifier is aiming to maximize its robustness to the strategic “manipulations”. This talk revisits a couple of dynamics concepts in the above formulation. The first question we try to revisit is: are all changes considered undesirable? We observe that in many application settings, changes in agents’ profile X can lead to true improvement in their target variable Y [1,2]. This observation requires us to revisit the objective function of the learner, and study the possibility of inducing an improved population from the agents. The second question we revisit is: do agents respond rationally? Inspired by evolutionary game theory, we introduce a dynamical agent response model using replicator dynamics to model agents’ potentially non-fully rational responses to a sequence of classifiers [3]. We characterize the dynamics of this model and offer observations of its fairness implication in such a long-term dynamical environment.

References:

[1] Linear Classifiers that Encourage Constructive Adaptation, Yatong Chen, Jialu Wang and Yang Liu, 2021.

[2] Induced Domain Adaptation, Yang Liu, Yatong Chen, Jiaheng Wei, 2021.

[3] Unintended Selection: Persistent Qualification Rate Disparities and Interventions, Reilly Raab and Yang Liu, Neural Information Processing Systems (NeurIPS), 2021