FAIRNES AND BIAS IN MACHINE LEARNING MODELS

被引:0
|
作者
Langworthy, Andrew [1 ]
机构
[1] University of East Anglia., United Kingdom
来源
Journal of the Institute of Telecommunications Professionals | 2023年 / 17卷
关键词
Risk assessment;
D O I
暂无
中图分类号
学科分类号
摘要
In recent decades the volume of data generated by businesses and consumers has rocketed, from information on location, buying habits, browsing activity and more. With this data boom comes the opportunity to exploit that data for commercial gain. Machine learning is the way to do this, an actively developing field, with improvements to speed and scalability happening at pace. With these come the risks of biases in the data or the models used to exploit them. As with all advancements, the understanding of these risks is still developing, and care must be taken to both measure and mitigate them. © 2023 Institute of Telecommunications Professionals. All rights reserved.
引用
收藏
页码:29 / 33
相关论文
共 50 条
  • [21] Laboratory Data as a Potential Source of Bias in Healthcare Artificial Intelligence and Machine Learning Models
    Luu, Hung S.
    ANNALS OF LABORATORY MEDICINE, 2025, 45 (01) : 12 - 21
  • [22] Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
    Kovacs, David Peter
    McCorkindale, William
    Lee, Alpha A.
    NATURE COMMUNICATIONS, 2021, 12 (01)
  • [23] Risk of bias of prognostic models developed using machine learning: a systematic review in oncology
    Dhiman, Paula
    Ma, Jie
    Navarro, Constanza L. Andaur
    Speich, Benjamin
    Bullock, Garrett
    Damen, Johanna A. A.
    Hooft, Lotty
    Kirtley, Shona
    Riley, Richard D.
    Van Calster, Ben
    Moons, Karel G. M.
    Collins, Gary S.
    DIAGNOSTIC AND PROGNOSTIC RESEARCH, 2022, 6 (01)
  • [24] Applying machine learning models in multi-institutional studies can generate bias
    Fussell, Rebeckah K.
    Sundstrom, Meagan
    McDowell, Sabrina
    Holmes, N. G.
    2024 PHYSICS EDUCATION RESEARCH CONFERENCE, PERC, 2024, : 144 - 149
  • [25] Machine-learning media bias
    D'Alonzo, Samantha
    Tegmark, Max
    PLOS ONE, 2022, 17 (08):
  • [26] Ethical Implications Of Bias In Machine Learning
    Yapo, Adrienne
    Weiss, Joseph
    PROCEEDINGS OF THE 51ST ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS), 2018, : 5365 - 5372
  • [27] Mitigating Racial Bias in Machine Learning
    Kostick-Quenet, Kristin M.
    Cohen, I. Glenn
    Gerke, Sara
    Lo, Bernard
    Antaki, James
    Movahedi, Faezah
    Njah, Hasna
    Schoen, Lauren
    Estep, Jerry E.
    Blumenthal-Barby, J. S.
    JOURNAL OF LAW MEDICINE & ETHICS, 2022, 50 (01): : 92 - 100
  • [28] A Survey on Bias and Fairness in Machine Learning
    Mehrabi, Ninareh
    Morstatter, Fred
    Saxena, Nripsuta
    Lerman, Kristina
    Galstyan, Aram
    ACM COMPUTING SURVEYS, 2021, 54 (06)
  • [29] Simplicity Bias in Overparameterized Machine Learning
    Berchenko, Yakir
    THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 10, 2024, : 11052 - 11060
  • [30] Bias in Machine Learning: A Literature Review
    Mavrogiorgos, Konstantinos
    Kiourtis, Athanasios
    Mavrogiorgou, Argyro
    Menychtas, Andreas
    Kyriazis, Dimosthenis
    APPLIED SCIENCES-BASEL, 2024, 14 (19):