OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning
OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning
复制标题
OmniFair：机器学习中与模型无关的群体公平性的声明系统
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Navathe
中科院分区：
文献类型：
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作者：
Hantian Zhang;Xu Chu;Abolfazl Asudeh;S. Navathe
机器学习（ML）越来越多地用于我们社会的决策。然而，ML模型可能对某些人口统计群体不公平（例如，非裔美国人或女性）根据各种公平指标。用于产生公平ML模型的现有技术受限于它们可以处理的公平性约束的类型（例如，预处理）或需要对下游ML训练算法进行重要修改（例如，处理中）。我们提出了一个声明式系统OmniFair，用于支持ML中的组公平性。OmniFair具有一个声明式接口，供用户指定所需的组公平性约束，并支持所有常用的组公平性概念，包括统计奇偶性，均衡赔率和预测奇偶性。OmniFair也是模型无关的，因为它不需要修改所选的ML算法。OmniFair还支持同时执行多个用户声明的公平性约束，而大多数以前的技术不能。OmniFair中的算法在满足指定公平性约束的同时最大化模型准确性，并且基于理论上可证明的关于准确性和公平性之间的权衡的单调性属性优化其效率，这是我们系统所独有的。我们在公平文献中对少数群体表现出偏见的常用数据集上进行实验。我们表明，OmniFair在支持的公平性约束和下游ML模型方面比现有的算法公平性方法更通用。与第二好的方法相比，OmniFair减少了高达94.8%的准确性损失。OmniFair还实现了与预处理方法相似的运行时间，并且比处理中方法快270倍。
Machine learning (ML) is increasingly being used to make decisions in our society. ML models, however, can be unfair to certain demographic groups (e.g., African Americans or females) according to various fairness metrics. Existing techniques for producing fair ML models either are limited to the type of fairness constraints they can handle (e.g., preprocessing) or require nontrivial modifications to downstream ML training algorithms (e.g., in-processing). We propose a declarative system OmniFair for supporting group fairness in ML. OmniFair features a declarative interface for users to specify desired group fairness constraints and supports all commonly used group fairness notions, including statistical parity, equalized odds, and predictive parity. OmniFair is also model-agnostic in the sense that it does not require modifications to a chosen ML algorithm. OmniFair also supports enforcing multiple user declared fairness constraints simultaneously while most previous techniques cannot. The algorithms in OmniFair maximize model accuracy while meeting the specified fairness constraints, and their efficiency is optimized based on the theoretically provable monotonicity property regarding the trade-off between accuracy and fairness that is unique to our system. We conduct experiments on commonly used datasets that exhibit bias against minority groups in the fairness literature. We show that OmniFair is more versatile than existing algorithmic fairness approaches in terms of both supported fairness constraints and downstream ML models. OmniFair reduces the accuracy loss by up to 94.8% compared with the second best method. OmniFair also achieves similar running time to preprocessing methods, and is up to 270x faster than in-processing methods.
FairPrep：在增强公平干预措施的研究中向一流公民推广数据
DOI：
10.5441/002/edbt.2020.41
发表时间：
2020
期刊：
2020
影响因子：
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作者：
Schelter, Sebastian;He, Yuxuan;Khilnani, Jatin;Stoyanovich, Julia
通讯作者：
Stoyanovich, Julia
DOI：
10.1145/3318464.3389709
发表时间：
2020-05
期刊：
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
影响因子：
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作者：
Supun Nakandala;Arun Kumar
通讯作者：
Supun Nakandala;Arun Kumar
DOI：
10.14778/3297753.3297763
发表时间：
2018-12
期刊：
ArXiv
影响因子：
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作者：
Doris Xin;Stephen Macke;Litian Ma;Jialin Liu;Shuchen Song;Aditya G. Parameswaran
通讯作者：
Doris Xin;Stephen Macke;Litian Ma;Jialin Liu;Shuchen Song;Aditya G. Parameswaran