Bias, Language, and Medical Data: Guidelines for Ethical Data Repositories and Applications
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Authors
Sauve, Annabelle
Date
Type
thesis
Language
eng
Keyword
bias , data , ethics
Alternative Title
Abstract
This thesis argues that biases in healthcare data can have harmful effects on patients by influencing the level of care they receive and that the use of artificial intelligence (AI) can further exacerbate this problem by incorporating biased representations into predictions. To this end, we analyze various sources of medical data and the many implications that come with using personal information, and highlight the different types of bias, including biases in medical text and algorithmic biases. We hypothesize that the limited number of publicly available health data repositories contributes to the existence of bias in medical computing research. To address this issue, we propose two novel medical data dictionaries using primary care data, which is a largely unexplored data source. Specifically, we present a domain-specific primary care data dictionary for mental health diagnoses and preliminary work toward a medical bias remover dictionary, along with guidelines for ethically creating this type of data compilation. Additionally, we contribute to the discussion of ethical medical AI by reviewing existing principles of medical ethics and presenting suggestions for addressing the patient and industry benefit trade-off that is common in AI research. This thesis highlights the importance of addressing bias in healthcare data and related applications to ensure that patients receive the care they deserve, and proposes guidelines to do so by focusing on primary care data.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution 3.0 United States
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution 3.0 United States