Paper Session 19
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Fair Clustering via Equitable Group Representations
What does it mean for a clustering to be fair? One popular approach seeks to ensure that each cluster contains groups in (roughly) the same proportion in which they exist in the population. The normative principle at play is balance: any cluster might act as a representative of the data, and thus should reflect its diversity. But clustering also captures a different form of representativeness. A core principle in most clustering problems is that a cluster center should be representative of the cluster it represents, by being “close" to the points associated with it. This is so that we can effectively replace the points by their cluster centers without significant loss in fidelity, and indeed is a common “use case” for clustering. For such a clustering to be fair, the centers should “represent” different groups equally well. We call such a clustering a group-representative clustering. In this paper, we study the structure and computation of group representative clusterings. We show that this notion naturally parallels the development of fairness notions in classification, with direct analogs of ideas like demographic parity and equal opportunity. We demonstrate how these notions are distinct from and cannot be captured by balance-based notions of fairness. We present approximation algorithms for group representative k-median clustering and couple this with an empirical evaluation on various real-world datasets. We also extend this idea to facility location, motivated by the current problem of assigning polling locations for voting.
A Pilot Study in Surveying Clinical Judgments to Evaluate Radiology Report Generation
The recent release of many Chest X-Ray datasets has prompted a lot of interest in radiology report generation. To date, this has been framed as an image captioning task, where the machine takes an RGB image as input and generates a 2-3 sentence summary of findings as output. The quality of these reports has been canonically measured using metrics from the NLP community for language generation such as Machine Translation and Summarization. However, the evaluation metrics (e.g. BLEU, CIDEr) are inappropriate for the medical domain, where clinical correctness is critical. To address this, our team brought together machine learning experts with radiologists for a pilot study in co-designing a better metric for evaluating the quality of an algorithmically-generated radiology report. The interdisciplinary collaborative process involved multiple interviews, outreach, and preliminary annotation to design a larger scale study – which is now underway – to build a more meaningful evaluation tool.