Report
Fair Clustering: A Causal Perspective
| العنوان: | Fair Clustering: A Causal Perspective |
|---|---|
| المؤلفون: | Bayer, Fritz, Plecko, Drago, Beerenwinkel, Niko, Kuipers, Jack |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Computers and Society, Computer Science - Machine Learning |
| الوصف: | Clustering algorithms may unintentionally propagate or intensify existing disparities, leading to unfair representations or biased decision-making. Current fair clustering methods rely on notions of fairness that do not capture any information on the underlying causal mechanisms. We show that optimising for non-causal fairness notions can paradoxically induce direct discriminatory effects from a causal standpoint. We present a clustering approach that incorporates causal fairness metrics to provide a more nuanced approach to fairness in unsupervised learning. Our approach enables the specification of the causal fairness metrics that should be minimised. We demonstrate the efficacy of our methodology using datasets known to harbour unfair biases. Comment: 14 pages |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.09061 |
| رقم الانضمام: | edsarx.2312.09061 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |