Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization. However, recent studies have noted the occurrence of bias (under- or over-representation of a certain gender or race) in such data summarization methods. In this paper we initiate a study of the problem of outputting a diverse and fair summary of a given dataset. We work with a well-studied determinantal measure of diversity and corresponding distributions (DPPs) and present a framework that allows us to incorporate a general class of fairness constraints into such distributions. Coming up with efficient algorithms to sample from these constrained determinantal distributions, however, suffers from a complexity barrier and we present a fast sampler that is provably good when the input vectors satisfy a natural property. Our experimental results on a real-world and an image dataset show that the diversity of the samples produced by adding fairness constraints is not too far from the unconstrained case, and we also provide a theoretical explanation of it.
Streaming Algorithms for Diversity Maximization with Fairness Constraints
East China Normal University（华东师范大学）；Universitat Pompeu Fabra（庞培法布拉大学）；Eurecat – Centre Tecnològic de Catalunya（Eurecat——加泰罗尼亚技术中心）；University of Helsinki（赫尔辛基大学）
Mind the Gap: A Review of Arabic Post-Training Datasets and Their Limitations
Refine AI；ASAS AI；University of Bisha（比沙大学）；University College London（伦敦大学学院）；King Salman Global Academy for Arabic（萨勒曼全球阿拉伯学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；King Abdulaziz University（阿卜杜勒阿齐兹大学）；HUMAIN
本文综述 Hugging Face Hub 上的阿拉伯语后训练数据集，从能力、可引导性、对齐和鲁棒性等维度评估其质量与局限，并提出未来建设建议。
