Predictive algorithms are now used to help distribute a large share of our society's resources and sanctions, such as healthcare, loans, criminal detentions, and tax audits. Under the right circumstances, these algorithms can improve the efficiency and equity of decision-making. At the same time, there is a danger that the algorithms themselves could entrench and exacerbate disparities, particularly along racial, ethnic, and gender lines. To help ensure their fairness, many researchers suggest that algorithms be subject to at least one of three constraints: (1) no use of legally protected features, such as race, ethnicity, and gender; (2) equal rates of "positive" decisions across groups; and (3) equal error rates across groups. Here we show that these constraints, while intuitively appealing, often worsen outcomes for individuals in marginalized groups, and can even leave all groups worse off. The inherent trade-off we identify between formal fairness constraints and welfare improvements -- particularly for the marginalized -- highlights the need for a more robust discussion on what it means for an algorithm to be "fair". We illustrate these ideas with examples from healthcare and the criminal-legal system, and make several proposals to help practitioners design more equitable algorithms.
Equitable Length of Stay Prediction for Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning
Loughborough University（拉夫堡大学）；Swansea University Medical School（斯旺西大学医学院）；University of Leicester（莱斯特大学）；Leicestershire Partnership NHS Trust（莱斯特郡合作 NHS 信托机构）
Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University School of Medicine（杜克大学医学院）；Duke AI Health（杜克人工智能健康中心）；Mass General Brigham（麻省总医院布里格姆）；Harvard Medical School（哈佛医学院）；National University of Singapore（新加坡国立大学）
Nostalgic Adam: Weighting more of the past gradients when designing the adaptive learning rate
Peking University（北京大学）；Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）；Center for Data Science, Peking University（北京大学数据科学中心）；Beijing Institute of Big Data Research（北京大数据研究院）
一阶优化算法已被证明在深度学习中非常突出。特别是RMSProp和Adam等算法非常受欢迎。然而，最近的工作指出，缺乏“长期记忆“的亚当类算法，这可能会妨碍他们的性能，并导致分歧。在我们的研究中，我们观察到，在设计自适应学习率时，加权更多的过去梯度是有好处的。因此，我们提出了一种算法，称为怀旧亚当（NosAdam），理论上保证在最佳已知的收敛速度收敛。NosAdam可以被认为是对Adam的非收敛问题的修复，以替代[Reddi et al.，2018年]。我们的初步数值实验表明，NosAdam是一个有前途的替代算法亚当。证明，代码和其他补充材料可以在匿名共享链接中找到。
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks
Research Organization of Information and Systems（信息与系统研究机构）；The Institute of Statistical Mathematics（统计数学研究所）；The Graduate University for Advanced Studies, SOKENDAI（综合研究大学院大学）；National Institute for Materials Science（国立材料科学研究所）
