OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning

被引:20
|
作者
Zhang, Hantian [1 ]
Chu, Xu [1 ]
Asudeh, Abolfazl [2 ]
Navathe, Shamkant B. [1 ]
机构
[1] Georgia Inst Technol, Atlanta, GA 30332 USA
[2] Univ Illinois, Chicago, IL USA
来源
SIGMOD '21: PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA | 2021年
关键词
Algorithmic Bias; Group Fairness; Declarative Systems;
D O I
10.1145/3448016.3452787
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 Omni Fair for supporting group fairness in ML. Omni Fair 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. Omni Fair is also model-agnostic in the sense that it does not require modifications to a chosen ML algorithm. Omni Fair also supports enforcing multiple user declared fairness constraints simultaneously while most previous techniques cannot. The algorithms in Omni Fair 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. Omni Fair also achieves similar running time to preprocessing methods, and is up to 270x faster than in-processing methods.
引用
收藏
页码:2076 / 2088
页数:13
相关论文
共 50 条
  • [41] A Model-Agnostic Approach for Learning with Noisy Labels of Arbitrary Distributions
    Hao, Shuang
    Li, Peng
    Wu, Renzhi
    Chu, Xu
    2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022), 2022, : 1219 - 1231
  • [42] Domain Generalization via Model-Agnostic Learning of Semantic Features
    Dou, Qi
    Castro, Daniel C.
    Kamnitsas, Konstantinos
    Glocker, Ben
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [43] Model-Agnostic Federated Learning for Privacy-Preserving Systems
    Almohri, Hussain M. J.
    Watson, Layne T.
    2023 IEEE SECURE DEVELOPMENT CONFERENCE, SECDEV, 2023, : 99 - 105
  • [44] Task-Robust Model-Agnostic Meta-Learning
    Collins, Liam
    Mokhtari, Aryan
    Shakkottai, Sanjay
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
  • [45] Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning
    Karia, Rushang
    Srivastava, Siddharth
    THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 8064 - 8073
  • [46] AcME-Accelerated model-agnostic explanations: Fast whitening of the machine-learning black box
    Dandolo, David
    Masiero, Chiara
    Carletti, Mattia
    Pezze, Davide Dalle
    Susto, Gian Antonio
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 214
  • [47] A Model-Agnostic Recommendation Explanation System Based on Knowledge Graph
    Chen, Yuhao
    Miyazaki, Jun
    DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2020, PT II, 2020, 12392 : 149 - 163
  • [48] Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
    Raymond, Christian
    Chen, Qi
    Xue, Bing
    Zhang, Mengjie
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 45 (11) : 13699 - 13714
  • [49] Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility
    Cui, Sen
    Pan, Weishen
    Zhang, Changshui
    Wang, Fei
    KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2021, : 207 - 217
  • [50] A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
    Si, Zihua
    Han, Xueran
    Xiao Zhang
    Jun Xu
    Yue Yin
    Yang Song
    Wen, Ji Rong
    PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22), 2022, : 224 - 233