On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
复制标题
关于算法决策策略的长期影响：通过社会学习的努力不公平和特征隔离
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
K. Gummadi
中科院分区：
文献类型：
--
作者：
Hoda Heidari;Vedant Nanda;K. Gummadi
大多数现有的算法公平概念都是一次性的：它们确保在决策时实现某种形式的分配平等，但没有考虑到今天的算法决定对某些人口部分的长期福利和繁荣的不利影响。我们对算法公平性有更广泛的看法。我们提出了一种基于努力的公平衡量标准，并提出了一个数据驱动的框架，用于表征算法策略对重塑潜在人口的长期影响。受有关社会学习的心理学文献和关于机会平等的经济学文献的启发，我们提出了一个关于个体如何对决策算法作出反应的微观模型。我们使用社会学和经济学中现有的隔离措施来量化由此产生的宏观规模人口水平的变化。重要的是，我们观察到，不同的模型可能会向不同的方向改变资质的群体条件分布。我们的发现提出了一些关于决策模型公平性形式化的重要问题。
Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population. We take a broader perspective on algorithmic fairness. We propose an effort-based measure of fairness and present a data-driven framework for characterizing the long-term impact of algorithmic policies on reshaping the underlying population. Motivated by the psychological literature on emph{social learning} and the economic literature on equality of opportunity, we propose a micro-scale model of how individuals may respond to decision-making algorithms. We employ existing measures of segregation from sociology and economics to quantify the resulting macro-scale population-level change. Importantly, we observe that different models may shift the group-conditional distribution of qualifications in different directions. Our findings raise a number of important questions regarding the formalization of fairness for decision-making models.
DOI：
--
发表时间：
2019
期刊：
In Proceedings of ACM FAT*
影响因子：
--
作者：
Milli, Smitha;Miller, John;Dragan, Anca;Hardt, Moritz
通讯作者：
Hardt, Moritz