Funder
National Natural Science Foundation of China
Australian Research Council
Reference50 articles.
1. Algorithmic justice: Algorithms and big data in criminal justice settings;Završnik;Eur. J. Criminol.,2019
2. H. Kaur, H. Nori, S. Jenkins, R. Caruana, H. Wallach, J. Wortman Vaughan, Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning, in: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1–14.
3. Credit risk assessment using statistical and machine learning: basic methodology and risk modeling applications;Galindo;Comput. Econ.,2000
4. Cxplain: Causal explanations for model interpretation under uncertainty;Schwab,2019
5. J.J. Williams, J. Kim, A. Rafferty, S. Maldonado, K.Z. Gajos, W.S. Lasecki, N. Heffernan, Axis: Generating explanations at scale with learnersourcing and machine learning, in: Proceedings of the Third (2016) ACM Conference on Learning@ Scale, 2016, pp. 379–388.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献