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December 2, 2025Journal of Evidence-Based Social Work2 citations

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

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JHJohn C. HayvonJHJohn C. Hayvon

Key Points

  • Digital media enhancement significantly impacts participation in social justice efforts among marginalized groups, fostering inclusivity.
  • Qualitative data from semi-structured interviews with 8 individuals explored the experiences of stigma and barriers faced in digital media creation.
  • Employing an anti-oppressive framework, the research highlights audience receptivity and AI's role in shaping narratives in social work.
  • Understanding these dynamics calls for deeper engagement strategies to support marginalized voices in the digital media landscape.

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 digital-media creation tools 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 (n = 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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Cite This Study

Hayvon et al. (2025) studied this question.

synapsesocial.com/papers/692e3da16c9b3ab28c187cb2https://doi.org/10.1080/26408066.2025.2594675
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