The study of fairness in intelligent decision systems has mostly ignored
long-term influence on the underlying population. Yet fairness considerations
(e.g. affirmative action) have often the implicit goal of achieving balance
among groups within the population. The most basic notion of balance is
eventual equality between the qualifications of the groups. How can we
incorporate influence dynamics in decision making? How well do
dynamics-oblivious fairness policies fare in terms of reaching equality? In
this paper, we propose a simple yet revealing model that encompasses (1) a
selection process where an institution chooses from multiple groups according
to their qualifications so as to maximize an institutional utility and (2)
dynamics that govern the evolution of the groups' qualifications according to
the imposed policies. We focus on demographic parity as the formalism of
affirmative action.
We then give conditions under which an unconstrained policy reaches equality
on its own. In this case, surprisingly, imposing demographic parity may break
equality. When it doesn't, one would expect the additional constraint to reduce
utility, however, we show that utility may in fact increase. In more realistic
scenarios, unconstrained policies do not lead to equality. In such cases, we
show that although imposing demographic parity may remedy it, there is a danger
that groups settle at a worse set of qualifications. As a silver lining, we
also identify when the constraint not only leads to equality, but also improves
all groups. This gives quantifiable insight into both sides of the mismatch
hypothesis. These cases and trade-offs are instrumental in determining when and
how imposing demographic parity can be beneficial in selection processes, both
for the institution and for society on the long run.

本文研究了在智能决策系统中公平性的一个重大问题，即如何在决策过程中考虑人口的长期影响。我们提出了一个简单却深入的模型，它将考虑到人员选择以及政策对群体资格的影响力，重点研究肯定行动的形式主义。我们的研究明确了这个制约政策对于旨在实现平衡的肯定行动而言的成果，它是对智能决策系统中不断变化着的影响因素与公平性之间关系的重要探究。

从公平决策到社会平等

From Fair Decision Making to Social Equality

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.

该研究探讨了决策边界设计中策略行为和社会负担的关系，发现任何增加机构效用的努力都会导致相应的社会负担增加，因此需要在保证策略稳健性的前提下权衡社会福利和公平的考虑。