Fair and Interpretable Models for Survival Analysis
Fair and Interpretable Models for Survival Analysis
复制标题
公平且可解释的生存分析模型
DOI：
10.1145/3534678.3539259
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Purushotham, Sanjay
中科院分区：
文献类型：
--
作者：
Rahman, Md Mahmudur;Purushotham, Sanjay
生存分析旨在预测存在审查的情况下发生事件的风险，例如癌症死亡。最近的研究表明，现有的生存技术很容易对年龄、种族和/或性别等受保护的属性产生无意的偏见。例如，假设与预后和协变量无关（通常在实际数据中违反）的审查通常会导致对不同受保护群体的生存预测过高和有偏差。为了减少有害偏见并确保公平的生存预测，我们引入了基于生存函数和审查的公平定义。我们提出了新颖的公平且可解释的生存模型，该模型使用基于伪值的目标函数和公平性定义作为预测特定主题生存概率的约束。对三个现实世界生存数据集的实验表明，我们提出的公平生存模型在准确性和公平性衡量方面比现有生存技术有了显着改进。我们表明，我们提出的模型在不同类型和数量的审查下为受保护的属性提供了公平的预测。此外，我们研究可解释性和公平性之间的相互作用；并研究公平性和审查如何影响不同受保护属性的生存预测。
Survival analysis aims to predict the risk of an event, such as death due to cancer, in the presence of censoring. Recent research has shown that existing survival techniques are prone to unintentional biases towards protected attributes such as age, race, and/or gender. For example, censoring assumed to be unrelated to the prognosis and covariates (typically violated in real data) often leads to overestimation and biased survival predictions for different protected groups. In order to attenuate harmful bias and ensure fair survival predictions, we introduce fairness definitions based on survival functions and censoring. We propose novel fair and interpretable survival models which use pseudo valued-based objective functions with fairness definitions as constraints for predicting subject-specific survival probabilities. Experiments on three real-world survival datasets demonstrate that our proposed fair survival models show significant improvement over existing survival techniques in terms of accuracy and fairness measures. We show that our proposed models provide fair predictions for protected attributes under different types and amounts of censoring. Furthermore, we study the interplay between interpretability and fairness; and investigate how fairness and censoring impact survival predictions for different protected attributes.
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影响因子：
2
作者：
Antolini, L;Boracchi, P;Biganzoli, E
通讯作者：
Biganzoli, E
右审查偏差对生存分析的影响。
影响因子：
45.3
作者：
Enrique Barraj'on;Laura Barrajon
通讯作者：
Laura Barrajon
影响因子：
23.8
作者：
Mhasawade, Vishwali;Zhao, Yuan;Chunara, Rumi
通讯作者：
Chunara, Rumi
DOI：
10.1609/aaai.v35i1.16125
发表时间：
2021-05
期刊：
--
影响因子：
--
作者：
M. Rahman;K. Matsuo;S. Matsuzaki;S. Purushotham
通讯作者：
M. Rahman;K. Matsuo;S. Matsuzaki;S. Purushotham
影响因子：
8.9
作者：
Dispenzieri, Angela;Katzmann, Jerry A.;Rajkumar, S. Vincent
通讯作者：
Rajkumar, S. Vincent