Confounding factors need to be accounted for in assessing bias by machine learning algorithms

被引:12
|
作者
Mukherjee, Pritam [1 ]
Shen, Thomas C. [1 ]
Liu, Jianfei [1 ]
Mathai, Tejas [1 ]
Shafaat, Omid [1 ]
Summers, Ronald M. [1 ]
机构
[1] NIH, Imaging Biomarkers & Comp Aided Diag Lab, Dept Radiol & Imaging Sci, Hlth Clin Ctr, Bldg 10, Bethesda, MD 20892 USA
关键词
D O I
10.1038/s41591-022-01847-7
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
引用
收藏
页码:1159 / +
页数:3
相关论文
共 50 条
  • [1] Confounding factors need to be accounted for in assessing bias by machine learning algorithms
    Pritam Mukherjee
    Thomas C. Shen
    Jianfei Liu
    Tejas Mathai
    Omid Shafaat
    Ronald M. Summers
    [J]. Nature Medicine, 2022, 28 : 1159 - 1160
  • [2] Reply to: ‘Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms’ and ‘Confounding factors need to be accounted for in assessing bias by machine learning algorithms’
    Laleh Seyyed-Kalantari
    Haoran Zhang
    Matthew B. A. McDermott
    Irene Y. Chen
    Marzyeh Ghassemi
    [J]. Nature Medicine, 2022, 28 : 1161 - 1162
  • [3] Reply to: 'Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms' and 'Confounding factors need to be accounted for in assessing bias by machine learning algorithms'
    Seyyed-Kalantari, Laleh
    Zhang, Haoran
    McDermott, Matthew B. A.
    Chen, Irene Y.
    Ghassemi, Marzyeh
    [J]. NATURE MEDICINE, 2022, 28 (06) : 1161 - +
  • [4] Statistical quantification of confounding bias in machine learning models
    Spisak, Tamas
    [J]. GIGASCIENCE, 2022, 11
  • [5] Vectorization of Bias in Machine Learning Algorithms
    Bekerman, Sophie
    Chen, Eric
    Lin, Lily
    Nez, George D. Monta
    [J]. ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2, 2022, : 354 - 365
  • [6] Detecting racial bias in algorithms and machine learning
    Lee, Nicol Turner
    [J]. JOURNAL OF INFORMATION COMMUNICATION & ETHICS IN SOCIETY, 2018, 16 (03): : 252 - 260
  • [7] Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index
    Juhn, Young J.
    Ryu, Euijung
    Wi, Chung-Il
    King, Katherine S.
    Malik, Momin
    Romero-Brufau, Santiago
    Weng, Chunhua
    Sohn, Sunghwan
    Sharp, Richard R.
    Halamka, John D.
    [J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2022, 29 (07) : 1142 - 1151
  • [8] Investigating anatomical bias in clinical machine learning algorithms
    Pedersen, Jannik Skyttegaard
    Laursen, Martin Sundahl
    Vinholt, Pernille Just
    Alnor, Anne Bryde
    Savarimuthu, Thiusius Rajeeth
    [J]. 17TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EACL 2023, 2023, : 1398 - 1410
  • [9] Algorithmic Factors Influencing Bias in Machine Learning
    Blanzeisky, William
    Cunningham, Padraig
    [J]. MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021, PT I, 2021, 1524 : 559 - 574
  • [10] Exploring Bias and Fairness in Artificial Intelligence and Machine Learning Algorithms
    Khakurel, Utsab
    Abdelmoumin, Ghada
    Bajracharya, Aakriti
    Rawat, Danda B.
    [J]. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS APPLICATIONS IV, 2022, 12113