The Social Cost of Strategic Classification
This work addresses fairness and social welfare issues in machine learning for decision-making, highlighting negative externalities that affect individuals, particularly disadvantaged groups, making it a significant but incremental contribution to the field.
The paper tackles the problem of strategic classification in consequential decision-making, showing that efforts to increase institutional robustness by designing conservative decision boundaries benefit the decision maker at the expense of individuals, leading to increased social burden and disproportionate harm to disadvantaged groups.
Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule. A long line of work has therefore sought to counteract strategic behavior by designing more conservative decision boundaries in an effort to increase robustness to the effects of strategic covariate shift. We show that these efforts benefit the institutional decision maker at the expense of the individuals being classified. Introducing a notion of social burden, we prove that any increase in institutional utility necessarily leads to a corresponding increase in social burden. Moreover, we show that the negative externalities of strategic classification can disproportionately harm disadvantaged groups in the population. Our results highlight that strategy-robustness must be weighed against considerations of social welfare and fairness.