FAHT: An Adaptive Fairness-aware Decision Tree Classifier
By Wenbin Zhang, Eirini Ntoutsi. 2019
121 citations recorded by the source corpus.
Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well as offline services. These systems, depend on sophisticated learning algorithms and available data, to optimize the service function for decision support assistance. However, there is a growing concern about the accountability and fairness of the employed models by the fact that often the available historic data is intrinsically discriminatory, i.e., the proportion of members sharing one or more sensitive attributes is higher than the proportion in the population as a whole when receiving positive classification, which leads to a lack of fairness in decision support system. A number of fairness-aware learning methods have been proposed to handle this concern. However, these methods tackle fairness as a static problem and do not take the evolution of the underlying stream population into consideration. In this paper, we introduce a learning mechanism to design a fair classifier for online stream based decision-making. Our learning model, FAHT (Fairness-Aware Hoeffding Tree), is an extension of the well-known Hoeffding Tree algorithm for decision tree induction over streams, that also accounts for fairness. Our experiments show that our algorithm is able to deal with discrimination in streaming environments, while maintaining a moderate predictive performance over the stream.
Subfields: Artificial Intelligence, Computer Science Applications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Vasileios Iosifidis
- Julien Ferry
- Sébastien Gambs
- Eirini Ntoutsi
- Marie-José Huguet
- Mohamed Siala
- Ulrich Matchi Aïvodji
In Computer Science Applications
Citations in the corpus
- Learning Fair Rule Lists (2019, methodology): “Zhang and Ntoutsi (2019) [65] 1: Summary of some statistical notions of fairness along with the related statistical measure to be equalized. by introducing the so-called fair information gain , which corresponds to the default accuracy gain if there is no unfairness or to the product of both…”
- AdaFair: Cumulative Fairness Adaptive Boosting (2019, methodology): “In [21, 34], for example, they embed the statistical parity fairness notion into the splitting criterion of a decision tree.”