Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (1): 266-272.doi: 10.11896/jsjkx.230500224

• Artificial Intelligence • Previous Articles     Next Articles

Fairness Metrics of Machine Learning:Review of Status,Challenges and Future Directions

ZHANG Wenqiong, LI Yun   

  1. School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    Jiangsu Key Laboratory of Big Data Security and Intelligent Processing,Nanjing 210003,China
  • Received:2023-05-30 Revised:2023-09-27 Online:2024-01-15 Published:2024-01-12
  • About author:ZHANG Wenqiong,born in 1998,Ph.D candidate.Her main researchinterestis fairness of machine learning.
    LI Yun,born in 1974,Ph.D,professor,Ph.D supervisor.His main research interests include machine learning and pattern recognition.
  • Supported by:
    Natural Science Fund for Colleges and Universities in Jiangsu Province(21KJA520003).

PDF (PC)

1881

Abstract: With the increasing popularity of machine learning applications,fairness of machine learning has attracted widespread attention from academia and industry,and has become an important component of trust-worthy artificial intelligence.To evaluate and improve the fairness of machine learning applications,a series of fairness metrics have been proposed by researchers.These metrics help to ensure fair decision-making of machine learning models among different individuals and groups,and provide gui-dance for improving and optimizing the model.However,there is still no consensus on the difference and correlation between these metrics,which are not clearly divided in different scenarios and tasks.This means that these fairness metrics lack a comprehensive classification system.In this paper,the fairness metrics are comprehensively organized and classified.Starting from the mathematical definition of these metrics,they are divided into two categories according to whether they are based on probability statistics.The two types of metrics are then further divided and elaborated separately.In order to facilitate readers’ understan-ding and application,combined with a practical case,the advantages and challenges of various metrics are pointed out in terms of application scenarios and implementation conditions,and the relationship between metrics is also discussed in conjunction with mathematical concepts,and possible future research directions are prospected.

Key words: Machine learning, Fairness of machine learning, Trust-worthy artificial intelligence, Fairness metrics, Fair decision

CLC Number: 

  • TP391
[1]MAHAJAN S,CARABALLO C,LU Y,et al.Trends in diffe-rences in health status and health care access and affordability by race and ethnicity in the United States,1999-2018[J].Jama,2021,326(7):637-648.
[2]CAUFFMAN C.Discrimination in online advertising[J].Maastricht Journal of European and Comparative Law,2021,28(3):283-286.
[3]LEE M S,FLORIDI L.Algorithmic fairness in mortgage len-ding:from absolute conditions to relational trade-offs[J].Minds and Machines,2021,31(1):165-191.
[4]XU G N,CHEN Y P,CHEN Y M,et al.Data Debiasing Method Based on Constrained Optimized Generative Adversarial Networks[J].Computer Science,2022,49(6A):184-190.
[5]PEDRESHI D,RUGGIERI S,TURINI F.Discrimination-aware data mining[C]//International Conference on Knowledge Discovery and Data Mining.ACM,2008:560-568.
[6]VERMA S,RUBIN J.Fairness definitions explained[C]//International Workshop on Software Fairness(Fairware).ACM,2018:1-7.
[7]MEHRABI N,MORSTATTER F,SAXENA N,et al.A survey on bias and fairness in machine learning[J].ACM Computing Surveys,2021,54(6):1-35.
[8]BERK R,HEIDARI H,JABBARI S,et al.Fairness in criminal justice risk assessments:The state of the art[J].Sociological Methods Research,2021,50(1):3-44.
[9]ZAFAR M B,VALERA I,GOMEZ R M,et al.Fairness beyond disparate treatment disparate impact:learning classification without disparate mistreatment[C]//International Conference on World Wide Web.ACM,2017:1171-1180.
[10]HARDT M,PRICE E,SREBRO N.Equality of opportunity in supervised learning[J].Advances in Neural Information Processing Systems,2016,29(1):3323-3331.
[11]CALDERS T,VERWER S.Three naive Bayes approaches for discrimination-free classification[J].Data Mining and Know-ledge Discovery,2010,21(2):277-292.
[12]JIANG Z,HAN X,FAN C,et al.Generalized demographic parity for group fairness[C]//International Conference on Learning Representations.OpenReview.net,2022.
[13]KAMIRAN F,ŽLIOBAITÈ I,CALDERS T.Quantifying ex-plainable discrimination and removing illegal discrimination in automated decision making[J].Knowledge and Information Systems,2013,35(3):613-644.
[14]XU R,CUI P,KUANG K,et al.Algorithmic decision making with conditional fairness[C]//International Conference on Knowledge Discovery and Data Mining.ACM,2020:2125-2135.
[15]FELDMAN M,FRIEDLER S A,MOELLER J,et al.Certifying and removing disparate impact[C]//International Conference on Knowledge Discovery and Data Mining.ACM,2015:259-268.
[16]STEWART R T.Identity and the limits of fair assessment[J].Journal of Theoretical Politics,2022,34(3):415-442.
[17]HEDDEN B.On statistical criteria of algorithmic fairness[J].Philosophy and Public Affairs,2021,49(2):209-231.
[18]CORBETT D S,PIERSON E,FELLER A,et al.Algorithmic decision making and the cost of fairness[C]//International Conference on Knowledge Discovery and Data Mining.ACM,2017:797-806.
[19]ZHAO H,GORDON G.Inherent tradeoffs in learning fair representations[J].Machine Learning Research,2022,23(57):1-26.
[20]DEHO O B,ZHAN C,LI J,et al.How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?[J].British Journal of Educational Technology,2022,53(4):822-843.
[21]BAROCAS S,SELBST A D.Big data’s disparate impact[J].Calif.L.Rev.,2016,104:671-732.
[22]CASTELNOVO A,CRUPI R,GRECO G,et al.A clarification of the nuances in the fairness metrics landscape[J].Scientific Reports,2022,12(1):4209.
[23]DWORK C,HARDT M,PITASSI T,et al.Fairness throughawareness[C]//International Conference on Innovations in Theoretical Computer Science.ACM,2012:214-226.
[24]LI A,PEARL J.Unit selection with causal diagram[C]//International Conference on Artificial Intelligence.AAAI,2022,36(5):5765-5772.
[25]GEBHART V,SMERZI A.Extending the fair sampling as-sumption using causal diagrams[J].Quantum,2023,7:897-906.
[26]KILBERTUS N,ROJAS C M,PARASCANDOLO G,et al.Avoiding discrimination through causal reasoning[J].Advances in Neural Information Processing Systems,2017,30(1):656-666.
[27]KIM H,SHIN S,JANG J H,et al.Counterfactual fairness with disentangled causal effect variational autoencoder[C]//International Conference on Artificial Intelligence.AAAI,2021,35(9):8128-8136.
[28]KUSNER M J,LOFTUS J,RUSSELL C,et al.Counterfactual fairness[J].Advances in Neural Information Processing Systems,2017,30(1):4066-4076.
[29]PUJOL D,MCKENNA R,KUPPAM S,et al.Fair decision ma-king using privacy-protected data[C]//International Conference on Fairness,Accountability,and Transparency.ACM,2020:189-199.
[30]CZARNOWSKA P,VYAS Y,SHAH K.Quantifying social biases in nlp:a generalization and empirical comparison of extrinsic fairness metrics[J].Transactions of the Association for Computational Linguistics,2021,9(1):1249-1267.
[31]GAO R,GE Y,SHAH C.FAIR:Fairness-aware information retrieval evaluation[J].Association for Information Science and Technology,2022,73(10):1461-1473.
[32]FRIEDLER S A,SCHEIDEGGER C,VENKATASUBRAMANIAN S,et al.A comparative study of fairness-enhancing interventions in machine learning[C]//International Conference on Fairness,Accountability,and Transparency.ACM,2019:329-338.
[33]KLEINBERG J,MULLAINATHAN S,RAGHAVAN M.In-herent trade-offs in the fair determination of risk scores[C]//International Conference on Innovations in Theoretical Compu-ter Science.Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik,2017:43:1-43:23.
[34]LIU L T,DEAN S,ROLF E,et al.Delayed impact of fair machine learning[C]//International Conference on Machine Lear-ning.PMLR,2018:3150-3158.
[35]BOUVATIER V,EL OUARDI S.Credit gaps as banking crisis predictors:a different tune for middle-and low-income countries[J].Emerging Markets Review,2023,54(C):101001.
[36]FARNADI G,BABAKI B,GETOOR L.Fairness in relational domains[C]//International Conference on AI,Ethics,and Society.ACM,2018:108-114.
[37]CHAO L M,YIN X L.AI Governance and System:Current Si-tuation and Trend[J].Computer Science,2021,48(9):1-8.
[38]HUGGINS-MANLEY A C,BOOTH B M,D’ELLO S K.To-ward Argument-Based Fairness with an Application to AI-Enhanced Educational Assessments[J].Journal of Educational Measurement,2022,59(3):362-388.
[39]GICHOYA J W,MCCOY L G,CELI L A,et al.Equity in essence:a call for operationalising fairness in machine learning for healthcare[J].BMJ Health & Care Informatics,2021,28(1):e100289.
[40]KALLUS N,ZHOU A.Fairness,welfare,and equity in perso-nalized pricing[C]//Proceedings of the 2021 ACM Conference on Fairness,Accountability,and Transparency.2021:296-314.
[41]BAKER R S,HAWN A.Algorithmic bias in education[J].International Journal of Artificial Intelligence in Education,2022,32(1):1052-1092.
[42]REDDY S,ALLAN S,COGHLAN S,et al.A governance model for the application of AI in health care[J].Journal of the American Medical Informatics Association,2020,27(3):491-497.
[43]DELOBELLE P,TOKPO E K,CALDERS T,et al.Measuring fairness with biased rulers:A comparative study on bias metrics for pre-trained language models[C]//The 2022 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies(NAACL 2022).2022:1693-1706.
[1] LI Haixia, SONG Danlei, KONG Jianing, SONG Yafei, CHANG Haiyan. Evaluation of Hyperparameter Optimization Techniques for Traditional Machine Learning Models [J]. Computer Science, 2024, 51(8): 242-255.
[2] ZHANG Daili, WANG Tinghua, ZHU Xinglin. Overview of Sample Reduction Algorithms for Support Vector Machine [J]. Computer Science, 2024, 51(7): 59-70.
[3] ZHOU Tianyang, YANG Lei. Study on Client Selection Strategy and Dataset Partition in Federated Learning Basedon Edge TB [J]. Computer Science, 2024, 51(6A): 230800046-6.
[4] SI Jia, LIANG Jianfeng, XIE Shuo, DENG Yingjun. Research Progress of Anomaly Detection in IaaS Cloud Operation Driven by Deep Learning [J]. Computer Science, 2024, 51(6A): 230400016-8.
[5] WANG Zhaodan, ZOU Weiqin, LIU Wenjie. Buggy File Identification Based on Recommendation Lists [J]. Computer Science, 2024, 51(6A): 230600088-8.
[6] LIU Wei, SONG You, ZHUO Peiyan, WU Weiqiang, LIAN Xin. Study on Kcore-GCN Anti-fraud Algorithm Fusing Multi-source Graph Features [J]. Computer Science, 2024, 51(6A): 230600040-7.
[7] CHEN Xiangxiao, CUI Xin, DU Qin, TANG Haoyao. Study on Optimization of Abnormal Traffic Detection Model Based on Machine Learning [J]. Computer Science, 2024, 51(6A): 230700051-5.
[8] TIAN Shuaihua, LI Zheng, WU Yonghao, LIU Yong. Identifying Coincidental Correct Test Cases Based on Machine Learning [J]. Computer Science, 2024, 51(6): 68-77.
[9] LIN Binwei, YU Zhiyong, HUANG Fangwan, GUO Xianwei. Data Completion and Prediction of Street Parking Spaces Based on Transformer [J]. Computer Science, 2024, 51(4): 165-173.
[10] WANG Degang, SUN Yi, GAO Qi. Active Membership Inference Attack Method Based on Multiple Redundant Neurons [J]. Computer Science, 2024, 51(4): 373-380.
[11] WANG Xin, HUANG Weikou, SUN Lingyun. Survey of Incentive Mechanism for Cross-silo Federated Learning [J]. Computer Science, 2024, 51(3): 20-29.
[12] LI Meng, DAI Haipeng, SUI Yongxi, GU Rong, CHEN Guihai. Survey of Learning-based Filters [J]. Computer Science, 2024, 51(1): 41-49.
[13] FU Jianming, JIANG Yuqian, HE Jia, ZHENG Rui, SURI Guga, PENG Guojun. Cryptocurrency Mining Malware Detection Method Based on Sample Embedding [J]. Computer Science, 2024, 51(1): 327-334.
[14] HUANG Shuxin, ZHANG Quanxin, WANG Yajie, ZHANG Yaoyuan, LI Yuanzhang. Research Progress of Backdoor Attacks in Deep Neural Networks [J]. Computer Science, 2023, 50(9): 52-61.
[15] WANG Yao, LI Yi. Termination Analysis of Single Path Loop Programs Based on Iterative Trajectory Division [J]. Computer Science, 2023, 50(9): 108-116.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.