News  |  ,   |  February 12, 2020

Algorithm Groups People More Fairly to Reduce AI Bias

News article by By Matthew Hutson.
Published by IEEE Spectrum.


Fair clustering algorithms aim to make artificial intelligence more impartial.

Say you work at a big company and you’re hiring for a new position. You receive thousands of resumes. To make a first pass, you may turn to artificial intelligence. A common AI task is called clustering, in which an algorithm sorts through a set of items (or people) and groups them into similar clusters.

In the hiring scenario, you might create clusters based on skills and experience and then hire from the top crop. But algorithms can be unfair. Even if you instruct them to ignore factors like gender and ethnicity, these attributes often correlate with factors you do count, leading to clusters that don’t represent the larger pool’s demographics. As a result, you could end up hiring only white men.

In recent years, computer scientists have constructed fair clustering algorithms to counteract such biases, and a new one offers several advantages over those that came before. It could improve fair clustering, whether the clusters contain job candidates, customers, sick patients, or potential criminals. [ . . . ]

The Research

Fairness in Clustering with Multiple Sensitive Attributes
By Savitha Sam Abraham, Deepak P, Sowmya S Sundaram
Proceedings of the 23rd International Conference on Extending Database Technology (EDBT 2020), 30th March-2nd April, 2020
Abstract: A clustering may be considered as fair on pre-specified sensitive attributes if the proportions of sensitive attribute groups in each cluster reflect that in the dataset. In this paper, we consider the task of fair clustering for scenarios involving multiple multi-valued or numeric sensitive attributes. We propose a fair clustering method, \textit{FairKM} (Fair K-Means), that is inspired by the popular K-Means clustering formulation. We outline a computational notion of fairness which is used along with a cluster coherence objective, to yield the FairKM clustering method. We empirically evaluate our approach, wherein we quantify both the quality and fairness of clusters, over real-world datasets. Our experimental evaluation illustrates that the clusters generated by FairKM fare significantly better on both clustering quality and fair representation of sensitive attribute groups compared to the clusters from a state-of-the-art baseline fair clustering method.