Dataset Groups Activity Stream SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification The authors used two tabular datasets, Adult and Credit, that are ubiquitous in fairness literature, and transformed them into multi-label settings. BibTex: @dataset{Tianci_Liu_and_Haoyu_Wang_and_Yaqing_Wang_and_Xiaoqian_Wang_and_Lu_Su_and_Jing_Gao_2024, abstract = {The authors used two tabular datasets, Adult and Credit, that are ubiquitous in fairness literature, and transformed them into multi-label settings.}, author = {Tianci Liu and Haoyu Wang and Yaqing Wang and Xiaoqian Wang and Lu Su and Jing Gao}, doi = {10.57702/mc83pca6}, institution = {No Organization}, keyword = {'Adult Dataset', 'Credit Dataset', 'Fairness', 'Multi-Label Classification'}, month = {dec}, publisher = {TIB}, title = {SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification}, url = {https://service.tib.eu/ldmservice/dataset/simfair--a-uni-ed-framework-for-fairness-aware-multi-label-classi-cation}, year = {2024} }
