Anthro-Digital Narratives (ADN) framework & EthnoGo mobile app prototype V1

Software


Masumi, M. 2025. Anthro-Digital Narratives (ADN) framework & EthnoGo mobile app prototype V1.
AuthorsMasumi, M.
Abstract

I have created and developed a new composite theoretical and methodological research framework called Anthro-Digital Narratives (ADN), along with the mobile app software to implement it. Through the ADN framework, I will examine algorithmic bias and the visibility of Black British female art on Instagram, TikTok, and Twitter/X. This decolonial methodology integrates anthropology, digital humanities, ethical artificial intelligence (AI), and a mobile-first approach.

Using the EthnoGo app, which I also created and developed, a no-code mobile app, I will research platform-driven misrepresentations of Black British identity, co-create counter-strategies with artists, and advocate for equitable AI policies. Over 12 weeks, EthnoGo’s design will facilitate multimodal data collection (posts, engagement metrics, AI-generated content) and participatory workshops to analyse disparities in algorithmic reach and moderation. Ethical priorities include data sovereignty, ensuring artists retain ownership of cultural outputs, and auditing AI tools for racial and gender bias. Although limited by restricted access to proprietary algorithmic data, the study prioritises transparency through mobile-first methods, publicly available metrics, and community validation. The findings aim to inform policy reforms and demonstrate how mobile-first, inclusive methodologies like ADN and EthnoGo can challenge systemic inequities in digital cultural representation.

KeywordsAnthro-Digital Narratives (ADN); Algorithmic bias; Black British female art; Social media; Decolonial methodology; Anthropology; Digital humanities; Ethical artificial intelligence (AI); Mobile-first approach; EthnoGo app; No-code; Platform-driven misrepresentations; Counter-strategies; Equitable AI policies; Multimodal data collection; Participatory workshops; Algorithmic reach; Moderation; Data sovereignty; Cultural outputs; Racial and gender bias; Transparency; Metrics; Community validation; Policy reforms; Systemic inequalities; Digital cultural representation; Glide
Year2025
Official URLhttps://ethnog-adn-v1-6zbb.glide.page
Publisher's version
License
File Access Level
Restricted
Output statusPublished
Publication dates
Print22 Mar 2025
Publication process dates
Accepted22 Mar 2025
Deposited31 Mar 2025
References

Benjamin, R. (2019) Race After Technology: Abolitionist Tools for the New Jim Code. Cambridge: Polity Press.
Buolamwini, J. and Gebru, T. (2018) ‘Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification’. Proceedings of Machine Learning Research, 81, pp. 1–15.
Chilisa, B. (2012) Indigenous Research Methodologies. London: SAGE.
Hall, S. (1997) Representation: Cultural Representations and Signifying Practices. London: SAGE.
Noble, S. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
Tuhiwai Smith, L. (2012) Decolonizing Methodologies: Research and Indigenous Peoples. 2nd edn. London: Zed Books.
Universities UK. (2024). Higher education in numbers. [online] Available at: https://www.universitiesuk.ac.uk/latest/insights-and-analysis/higher... [Accessed 29 Mar. 2025].
Inside Higher Ed. (2024). Doctoral Education in the UK Is in Trouble. [online] Available at: https://www.insidehighered.com/news/global/2024/01/12/doctoral-educa... [Accessed 29 Mar. 2025].

Additional information

This development is still ongoing and awaiting Patent process to be completed therefore will remain safeguarded for a period of time.

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