Applying a community‐engaged participatory machine learning model.
Although predictive algorithms have been described as the definitive solution to bias in health care, machine learning techniques may also propagate existing health inequities within the community context. However, there may be ways in which machine learning techniques can help community psychologis...
| Publicado en: | American Journal of Community Psychology Vol. 74; no. 3/4; pp. 262 - 269 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
Dec2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=181921557&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 181921557 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00910562 CMP jtl: American Journal of Community Psychology issn: 00910562 maglogo: N pubinfo: dt: Dec2024 vid: 74 iid: 3/4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 181921557 10.1002/ajcp.12765 ppf: 262 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 865KB tig: atl: Applying a community‐engaged participatory machine learning model. aug: au: Asabor, Emmanuella Ngozi Aneni, Kammarauche Weerakoon, Sitara Opara, Ijeoma affil: Yale School of Medicine, New Haven Connecticut,, USA Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven Connecticut,, USA Child Study Center, Yale School of Medicine, New Haven Connecticut,, USA Yale University School of Medicine, New Haven Connecticut,, USA Department of Social & Behavioral Sciences, Yale School of Public Health, New Haven Connecticut,, USA Department of Psychiatry, Yale School of Medicine, The Consultation Center, New Haven Connecticut,, USA su: Health equity Participant observation Self-efficacy Community psychology Machine learning Transformative learning sug: subj: Health equity Participant observation Self-efficacy Community psychology Machine learning Transformative learning keyword: bias in health care biased algorithms community‐based participatory research machine learning racism bias in health care biased algorithms community‐based participatory research machine learning racism ab: Although predictive algorithms have been described as the definitive solution to bias in health care, machine learning techniques may also propagate existing health inequities within the community context. However, there may be ways in which machine learning techniques can help community psychologists, public health researchers and practitioners identify patterns in data in a way that empowers improved outcomes. Incorporating community insight in all stages of machine learning research mitigates bias by positioning members of underrepresented communities as the experts of their lived experiences. As community psychologists already prioritize community‐based participatory practices, we propose three core guiding principles for a community‐engaged participatory model for research using machine learning techniques: shared decision‐making, reflexivity and structural humility, and flexibility and adaptability. Guided by these three principles, we emphasize grounding priority setting, problem formation, model assumptions, and interpretation of the resulting algorithmic patterns in the truths born from the lived experiences of people closest to the problem. We also suggest opportunities for bidirectional and mutually empowering partnerships between algorithmic scientists and the communities to which their algorithms will be applied. Inclusion of community stakeholders in all stages of machine learning for health research provides an opportunity to develop algorithms that are both highly effective and ethically grounded in the lived experiences of target populations. Highlights: The fusion of participatory research principles with cutting‐edge machine learning techniques.A transformative framework that addresses complex challenges while ensuring inclusivity in research.This work catalyzes a paradigm shift in both community psychology and artificial. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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