Digital Media for Social Justice and Change: Conceptualizing Impacts of Artificial Intelligence on Marginalized Media Creators

Existing research documents that in a technologically connected society, digital media can often shift population-level ideologies surrounding social justice and social work. Additionally, evidence indicates increased digital-media consumption patterns given how marginalized individuals can face greater barriers in physical participation. Based upon such rationale, this conceptual paper investigates how artificial intelligence serving as <i>digital-media creation tools</i> may impact the lived experience of those who face marginalization due to age, gender, race, Indigenous ancestry, rural geography, disability, and socioeconomic status. This paper reports qualitative data from a parent study engaging with marginalized individuals (<i>n</i> = 8) experiencing 1) intersectional statuses associated with stigma and 2) ongoing barriers to participation in formal learning opportunities, to assess how digital media play critical roles in shaping their access to new information, beliefs, and worldviews. Informed by anti-oppressive and trauma-informed principles in social work, the research employed semi-structured interviews guided by a collaboratively developed framework - CATER (Collection, Action, Transformation, Emotion, Recommendation). Participants reported an average of more than three statuses of marginalization, and were invited to share their lived experiences - specifically informing how marginalization impacts their autonomous creation of digital media and engagements with machine-learning technologies. A sevenpart framework of AI in social-work-oriented digital media creation is thus conceptualized to consider: inclusivity in narrative dissemination; financial barriers intersecting with socioeconomic status; dominant versus counternarratives; market influences; and AI's critical shortcomings in terms of visibility and audience receptivity. Implications for social justice and social work with marginalized groups conclude this study.

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Publication Details

Journal
Journal of Evidence-Based Social Work
Published
2025-11-28
DOI
https://doi.org/10.1080/26408066.2025.2594675
Primary Topic
Social Work Education and Practice
Type
article
Field-Weighted Citation Impact
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article

Digital Media for Social Justice and Change: Conceptualizing Impacts of Artificial Intelligence on Marginalized Media Creators

John C. Hayvon
Journal of Evidence-Based Social Work
Social Work Education and Practice
article

Digital Media for Social Justice and Change: Conceptualizing Impacts of Artificial Intelligence on Marginalized Media Creators

John C. Hayvon
article en

Abstract

Existing research documents that in a technologically connected society, digital media can often shift population-level ideologies surrounding social justice and social work. Additionally, evidence indicates increased digital-media consumption patterns given how marginalized individuals can face greater barriers in physical participation. Based upon such rationale, this conceptual paper investigates how artificial intelligence serving as <i>digital-media creation tools</i> may impact the lived experience of those who face marginalization due to age, gender, race, Indigenous ancestry, rural geography, disability, and socioeconomic status. This paper reports qualitative data from a parent study engaging with marginalized individuals (<i>n</i> = 8) experiencing 1) intersectional statuses associated with stigma and 2) ongoing barriers to participation in formal learning opportunities, to assess how digital media play critical roles in shaping their access to new information, beliefs, and worldviews. Informed by anti-oppressive and trauma-informed principles in social work, the research employed semi-structured interviews guided by a collaboratively developed framework - CATER (Collection, Action, Transformation, Emotion, Recommendation). Participants reported an average of more than three statuses of marginalization, and were invited to share their lived experiences - specifically informing how marginalization impacts their autonomous creation of digital media and engagements with machine-learning technologies. A sevenpart framework of AI in social-work-oriented digital media creation is thus conceptualized to consider: inclusivity in narrative dissemination; financial barriers intersecting with socioeconomic status; dominant versus counternarratives; market influences; and AI's critical shortcomings in terms of visibility and audience receptivity. Implications for social justice and social work with marginalized groups conclude this study.

Journal of Evidence-Based Social WorkVol. 23(1)
University of Alberta (CA)
Openalex Percentile: Top 55%
Social Work Education and Practice
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