A researcher from Queen's University Belfast has developed an algorithm that could help address the issue of artificial intelligence (AI) bias. Although AI has many applications, it also brings the risk of bias. As AI is trained using large volumes of data, if this data contains human biases, AI algorithms will make connections based on this. For example, if shown images of doctors that are predominantly male, AI will learn that doctors are less likely to be female. This creates a significant issue when the technology is used in recruitment, insurance or policing, as there is a danger of it reinforcing existing bias rather than help eliminate it.

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, 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.