Other Titles
Algorithmic Bias in Artificial Intelligence: Preparing Nurses for Critical and Culturally Safe Engagement [Title Slide]
Abstract
AI is increasingly used in healthcare and education, often framed as a tool for efficiency and objectivity. Yet, these systems are trained on data shaped by Western biomedical norms, reproducing structural and cultural biases. Deep learning does not create new knowledge; it reorganizes existing information. When that data-electronic health records, clinical trials, DSM-based tools- reflects Western perspectives, inequities are amplified under the guise of neutrality.
Consider mental health tools built on DSM frameworks, which pathologize non-Western expressions of distress. Despite the American Psychiatric Association’s 2021 acknowledgment of structural racism, AI-driven assessments still rely on instruments like PHQ-9 and GAD-7. Bias extends beyond mental health: race-based adjustments in kidney and pulmonary algorithms have delayed care for Black and Asian patients. Optum Health’s algorithm underestimated Black patients’ health risks by equating spending with need. These examples reveal how “precision” technologies can mask inequity.
For Indigenous peoples, algorithmic bias echoes colonial research practices. Frameworks like OCAP® (Ownership, Control, Access, Possession) offer ethical data governance grounded in sovereignty. Nursing education should adopt such models to prevent digital tools from perpetuating harm. Algorithmic bias is not a glitch, it reflects colonial logic embedded in data.
Teaching nurses to treat AI as neutral risks reinforcing inequities. Educators must foster critical AI literacy: questioning data origins, representation, and potential harms. Globally, nursing can shape AI integration to advance equity and cultural safety. Embedding these competencies aligns with the Truth and Reconciliation Commission’s Calls to Action and supports digital ethics in practice.
By developing critical engagement, nurses move from passive users to advocates for justice in digital health. Nursing curricula should integrate AI literacy, equity-focused frameworks, and critical data analysis to ensure technology serves all communities rather than reproducing systemic bias.
Notes
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References:
1. Couldry, N., & Mejias, U. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.
2. Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.
3. Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Polity Press.
4. Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020). Hidden in plain sight—Reconsidering the use of race correction in clinical algorithms. New England Journal of Medicine, 383(9), 874–882.
5. First Nations Information Governance Centre. (2020). The First Nations principles of OCAP®. https://fnigc.ca/ocap-training/
6. Obermeyer, Z., et al. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
7. Adams, R. (2021). Can artificial intelligence be decolonized? Interdisciplinary Science Reviews, 46(1–2), 176–197.
8. O’Connor, S., & Booth, R. G. (2022). Algorithmic bias in health care: Opportunities for nurses to improve equality in the age of artificial intelligence. Nursing Outlook, 70(6), 780-782.
9. Adams, R. (2021). Can artificial intelligence be decolonized? Interdisciplinary Science Reviews, 46(1–2), 176–197.
10. Huh, S. (2023). Ethical consideration of the use of generative artificial intelligence, including ChatGPT in writing a nursing article. Child Health Nursing Research, 29(4), 249–251.
11. Arora, P. (2024). Creative data justice: A decolonial and Indigenous framework to assess creativity and artificial intelligence. Information, Communication & Society. Advance online publication.
12. Mohamed, S., Png, M.-T., & Isaac, W. (2020). Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophy & Technology, 33(4), 659–684. https://doi.org/10.1007/s13347-020-00405-8
Sigma Membership
Non-member
Type
Presentation
Format Type
Text-based Document
Study Design/Type
N/A
Research Approach
N/A
Keywords:
Competence, Implementation Science, Faculty Development, Artificial Intelligence, AI, Health Care Delivery, Machine Learning Algorithms, Medical Ethics
Recommended Citation
Song, Claire; Gill, Tanvir; and Cawley, Pamela, "Algorithmic Bias in AI: Preparing Nurses for Critical and Culturally Safe Engagement" (2026). International Nursing Research Congress (INRC). 61.
https://www.sigmarepository.org/inrc/2026/presentations_2026/61
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
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All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-07-25
Algorithmic Bias in AI: Preparing Nurses for Critical and Culturally Safe Engagement
Toronto, Ontario, Canada
AI is increasingly used in healthcare and education, often framed as a tool for efficiency and objectivity. Yet, these systems are trained on data shaped by Western biomedical norms, reproducing structural and cultural biases. Deep learning does not create new knowledge; it reorganizes existing information. When that data-electronic health records, clinical trials, DSM-based tools- reflects Western perspectives, inequities are amplified under the guise of neutrality.
Consider mental health tools built on DSM frameworks, which pathologize non-Western expressions of distress. Despite the American Psychiatric Association’s 2021 acknowledgment of structural racism, AI-driven assessments still rely on instruments like PHQ-9 and GAD-7. Bias extends beyond mental health: race-based adjustments in kidney and pulmonary algorithms have delayed care for Black and Asian patients. Optum Health’s algorithm underestimated Black patients’ health risks by equating spending with need. These examples reveal how “precision” technologies can mask inequity.
For Indigenous peoples, algorithmic bias echoes colonial research practices. Frameworks like OCAP® (Ownership, Control, Access, Possession) offer ethical data governance grounded in sovereignty. Nursing education should adopt such models to prevent digital tools from perpetuating harm. Algorithmic bias is not a glitch, it reflects colonial logic embedded in data.
Teaching nurses to treat AI as neutral risks reinforcing inequities. Educators must foster critical AI literacy: questioning data origins, representation, and potential harms. Globally, nursing can shape AI integration to advance equity and cultural safety. Embedding these competencies aligns with the Truth and Reconciliation Commission’s Calls to Action and supports digital ethics in practice.
By developing critical engagement, nurses move from passive users to advocates for justice in digital health. Nursing curricula should integrate AI literacy, equity-focused frameworks, and critical data analysis to ensure technology serves all communities rather than reproducing systemic bias.
Description
AI is increasingly shaping healthcare and education, but it often reproduces bias rooted in colonial and racial hierarchies. This discussion explores how nursing education can foster critical AI literacy to prepare future nurses to challenge algorithmic inequities, promote cultural safety, and advance the Truth and Reconciliation Commission's Calls to Action.