How Do Fair Decisions Fare in Long-term Qualification?
This work addresses the long-term effects of fairness criteria on population dynamics, which is important for social science and policy, but it is incremental as it builds on existing fairness frameworks with simulated dynamics.
The paper tackles the problem of understanding the long-term impact of static fairness constraints on group well-being in algorithmic decision-making, finding that such constraints can either promote equality or exacerbate disparity depending on factors like qualification transitions and sensitive attributes.
Although many fairness criteria have been proposed for decision making, their long-term impact on the well-being of a population remains unclear. In this work, we study the dynamics of population qualification and algorithmic decisions under a partially observed Markov decision problem setting. By characterizing the equilibrium of such dynamics, we analyze the long-term impact of static fairness constraints on the equality and improvement of group well-being. Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions. We also consider possible interventions that can effectively improve group qualification or promote equality of group qualification. Our theoretical results and experiments on static real-world datasets with simulated dynamics show that our framework can be used to facilitate social science studies.