Preventing Disparate Treatment in Sequential Decision Making
Preventing Disparate Treatment in Sequential Decision Making
复制标题
防止顺序决策中的差别对待
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
A. Krause
中科院分区：
文献类型：
--
作者：
Hoda Heidari;A. Krause
我们研究了顺序决策环境中的公平性，在每个时间步，学习算法接收与新个体（例如新工作申请）相对应的数据，并且必须根据迄今为止的观察结果对他/她做出不可撤销的决定（例如是否雇用申请人）。为了防止不同的治疗情况，我们的时间依赖公平性概念要求算法决策是一致的：如果两个人在特征空间中相似，并且在同一时间段到达，算法必须将他们分配给相似的结果。我们提出了一个由黑盒学习模型进行后处理预测的一般框架，该框架保证了结果序列的一致性。我们从理论上表明，强加一致性不会显着减缓学习。我们在两个真实数据集上的实验在实践中说明并证实了这一发现。
We study fairness in sequential decision making environments, where at each time step a learning algorithm receives data corresponding to a new individual (e.g. a new job application) and must make an irrevocable decision about him/her (e.g. whether to hire the applicant) based on observations made so far. In order to prevent cases of disparate treatment, our time-dependent notion of fairness requires algorithmic decisions to be consistent: if two individuals are similar in the feature space and arrive during the same time epoch, the algorithm must assign them to similar outcomes. We propose a general framework for post-processing predictions made by a black-box learning model, that guarantees the resulting sequence of outcomes is consistent. We show theoretically that imposing consistency will not significantly slow down learning. Our experiments on two real-world data sets illustrate and confirm this finding in practice.
DOI：
10.1007/978-3-319-23461-8
发表时间：
2015
期刊：
--
影响因子：
--
作者：
J. Balcázar;F. Bonchi;A. Gionis;M. Sebag
通讯作者：
J. Balcázar;F. Bonchi;A. Gionis;M. Sebag