The Social Cost of Strategic Classification (1808.08460v2)

Abstract: 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.

• The paper introduces the 'social burden' metric to quantify the cost individuals incur when manipulating classifiers.
• The paper demonstrates that improving institutional utility beyond non-strategic optimality inevitably increases social burden, especially for disadvantaged groups.
• The paper analyzes fairness implications using FICO data, showing that threshold adjustments can exacerbate disparities between advantaged and disadvantaged populations.

Strategic Classification: Social Costs and Fairness

The paper "The Social Cost of Strategic Classification" rigorously investigates the tension between institutional utility and the social burden induced by strategic classification in machine learning. As institutions increasingly rely on machine learning models for consequential decision-making, individuals affected by these decisions adapt their behaviors, leading to a strategic covariate shift which can complicate the predictive validity of classifiers. This work undertakes a critical evaluation of existing approaches to robust classifiers in adversarial contexts and highlights the unintended social costs borne by individuals, particularly those from disadvantaged subpopulations.

The authors introduce a novel metric termed 'social burden' to quantify the strategic costs individuals incur when gaming classifiers to attain favorable decisions. With this, they expose an intrinsic trade-off between enhancing institutional utility and increasing social burden. Noteworthy is their proof of the inevitable increase in social burden when institutional utility improves beyond non-strategic optimal thresholds. The social burden inherently pertains to the costs and adaptive behaviors individuals must exhibit to be classified positively. These foundational insights refocus the discussion of strategic robustness from the institution-centric view to one that accounts for social welfare.

A significant contribution lies in the exploration of fairness across subpopulations within the framework of strategic classification. The paper models situations where disadvantaged groups suffer from disparate impacts due to different feature distributions or unequal adaptation costs. The authors analytically demonstrate that strategic classification can exacerbate existing disparities between advantaged and disadvantaged groups. Notably, they show the detrimental effects of threshold adjustments on these groups, with numerical demonstrations using FICO credit data substantiating their theoretical claims.

The paper introduces conditions under which strategic classification amplifies social gaps between groups, using intuitive scenarios in feature distributions and cost asymmetries. Under these conditions, varying thresholds for acceptance disproportionately penalized disadvantaged groups, evidenced in realistic setups such as FICO scores across racial groups. The analysis, grounded in formal statistical approaches and cost models, unveils the implications of strategic gaming, urging stakeholders to consider fairness impacts when designing or deploying machine learning-based classifiers.

The implications of this research extend theoretically and practically. The formalization of the strategic classification dilemma offers a robust framework for both AI ethics discussions and refinements of fairness metrics in socio-technological contexts. Practically, the insights serve as a critical guide for policy-making and algorithmic design, emphasizing the need for optimizing classifiers beyond mere institutional benefit to encompass societal welfare considerations. Future developments in AI, particularly those involving high-stakes decision-making, will invariably need to incorporate these perspectives, factoring in fairness alongside accuracy.

In conclusion, this paper delineates the complex interaction between strategic classifier design, institutional benefits, and social repercussions. It advocates for a balanced trade-off reflecting real-world complexities rather than simplistic robustness metrics, urging a redefinition of success within strategic classification paradigms. As machine learning models evolve, incorporating strategic classification insights will be imperative in fostering fair and equitable AI systems.