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
  • [31] Managing Bias in Machine Learning Projects
    Fahse, Tobias
    Huber, Viktoria
    van Giffen, Benjamin
    INNOVATION THROUGH INFORMATION SYSTEMS, VOL II: A COLLECTION OF LATEST RESEARCH ON TECHNOLOGY ISSUES, 2021, 47 : 94 - 109
  • [32] Mitigating bias in machine learning for medicine
    Vokinger, Kerstin N.
    Feuerriegel, Stefan
    Kesselheim, Aaron S.
    COMMUNICATIONS MEDICINE, 2021, 1 (01):
  • [33] Mitigating bias in machine learning for medicine
    Kerstin N. Vokinger
    Stefan Feuerriegel
    Aaron S. Kesselheim
    Communications Medicine, 1
  • [34] Detection and Evaluation of Machine Learning Bias
    Alelyani, Salem
    APPLIED SCIENCES-BASEL, 2021, 11 (14):
  • [35] Bias in Artificial Intelligence and Machine Learning
    Dube, Raghavi
    Shafana, Jeenath N.
    BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS, 2021, 14 (09): : 227 - 234
  • [36] Vectorization of Bias in Machine Learning Algorithms
    Bekerman, Sophie
    Chen, Eric
    Lin, Lily
    Nez, George D. Monta
    ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2, 2022, : 354 - 365
  • [37] Bias, machine learning, and conceptual engineering
    Rudolph, Rachel Etta
    Shech, Elay
    Tamir, Michael
    PHILOSOPHICAL STUDIES, 2025,
  • [38] Adversarial learning with optimism for bias reduction in machine learning
    Yu-Chen Cheng
    Po-An Chen
    Feng-Chi Chen
    Ya-Wen Cheng
    AI and Ethics, 2024, 4 (4): : 1389 - 1402
  • [39] Bias in Face Image Classification Machine Learning Models: The Impact of Annotator's Gender and Race
    Kafkalias, Andreas
    Herodotou, Stylianos
    Theodosiou, Zenonas
    Lanitis, Andreas
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2022, PART II, 2022, 647 : 89 - 100
  • [40] Bias correction for selecting the minimal-error classifier from many machine learning models
    Ding, Ying
    Tang, Shaowu
    Liao, Serena G.
    Jia, Jia
    Oesterreich, Steffi
    Lin, Yan
    Tseng, George C.
    BIOINFORMATICS, 2014, 30 (22) : 3152 - 3158