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Cited by 2Ethics and Social Impacts of AIDigital Media and Visual ArtExplainable Artificial Intelligence (XAI)

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Creating a Responsible AI Framework for Cultural Validity: Exploring Heritage Futures with an Ovahimba Indigenous Community of Namibia

Open access
Auala, Selma, Mbinge, Uariaike, Ndjao, Laurinda, van Niekerk, Keith Craig, Nathingo, Nestor, Nehoya, Ranta, Smith, Rachel Charlotte, Rodil, Kasper, Kapuire, Gereon Koch, Maasz, Donovan, Winschiers-Theophilus, Heike·2025·Proceedings of the Fifth Biennial African Human-Computer Interaction Conference
DOI 10.1145/3757232.3757268
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Abstract

This study introduces a Responsible AI (RAI) co-design framework for culturally valid representations, developed through community-based co-design with an Ovahimba community of Namibia. The framework emphasises three key pillars of process, purpose, and context, to ensure that AI systems accurately and respectfully represent indigenous knowledge and cultural values. To evaluate the framework, two Virtual Reality (VR) worlds were created: one AI-generated, which did not incorporate local cultural aspects, and the other designed using the framework to co-create culturally valid representations. Both VR worlds were assessed with members of the Ovahimba community. The results demonstrate the ability of the framework to guide RAI development that can safeguard cultural heritage and promote inclusive, accurate, and meaningful digital representations. This scalable approach offers a model for integrating cultural validity into AI systems across diverse communities.

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Crossref · 10.1145/3757232.3757268

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  • RAI must adapt to the socioeconomic and cultural contexts it seeks to serve.

    “Beyond aligning with high-level ethical principles, such as fairness, trustworthiness, transparency, and accountability, RAI must also adapt to the socioeconomic and cultural contexts it seeks to serve [8, 13, 20].”

    From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [8]

    Section 4.1 says responsible AI should be adapted to different cultural contexts through local co-design and attention to social structures. Section 4.3 also identifies class among the differences that AI development should account for. The paper does not clearly address adaptation to economic conditions more broadly, so the socioeconomic part of the claim is only partly supported. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • The community's principles included that representations should have a purpose and are always part of a wider context.

    “This included having a purpose and that representations are always part of a wider context [8].”

    From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [8]

  • Participants emphasized the importance of embedding every object and action within its cultural context, ensuring representations aligned with community traditions and narratives.

    “In particular, the participants emphasized the importance of embedding every object and action within its cultural context, ensuring representations aligned with community traditions and narratives [8].”

    From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [8]

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Citations

Cites

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Research works printed but not linked: 17

  1. Angie Abdilla Megan Kelleher Rick Shaw and Tyson Yunkaporta. 2021. Out of the Black Box: Indigenous Protocols for AI. https://hdl.handle.net/10536/DRO/DU:30159239 Melbourne: Deakin University.
  2. 10.1007/978-3-031-08215-3 · DOI · Paper page
  3. Sourojit Ghosh Pranav Narayanan Venkit Sanjana Gautam Shomir Wilson and Aylin Caliskan. 2024. Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach. Proceedings of the AAAI/ACM Conference on AI Ethics and Society 7 1 (Oct. 2024) 476–489. 10.1609/aies.v7i1.31651 · DOI
  4. 10.1007/978-1-349-27281-5 · DOI
  5. D. Maasz and H. Winschiers-Theophilus. 2016. Designing 3D Representations of Namibian Flora for Recognition by Indigenous Communities. In International Conference on Culture & Computer Science. none, Berlin, Germany, 77–86.
  6. 10.1007/978-981-10-7697-8_3 · DOI
  7. Blessing Mbalaka. 2023. Epistemically violent biases in artificial intelligence design: the case of DALLE-E 2 and Starry AI. Digital Transformation and Society 2 4 (2023) 376–402. · DOI
  8. Uariaike Mbinge Colin Stanley Immanuel Kandjabanga Chris Muashekele Kahuhu Alphons Koruhama Gereon Koch Kapuire Donovan Maasz and Heike Winschiers-Theophilus. 2025. Co-creating digital representations of indigenous knowledge: an ovaHimba curated digital repository. International Journal on Digital Libraries 26 1 (28 Feb 2025) 7. 10.1007/s00799-025-00418-8 · DOI · Paper page
  9. George Benneh Mensah. 2023. Artificial intelligence and ethics: a comprehensive review of bias mitigation transparency and accountability in AI Systems.
  10. 10.1145/3593743.3593761 · DOI
  11. Luke Munn. 2024. The five tests: designing and evaluating AI according to indigenous Māori principles. AI & SOCIETY 39 4 (2024) 1673–1681. · DOI
  12. Notice Pasipamire and Abton Muroyiwa. 2024. Navigating algorithm bias in AI: ensuring fairness and trust in Africa. Frontiers in Research Metrics and Analytics 9 (2024) 1486600. · DOI
  13. Rida Qadri Renee Shelby Cynthia L. Bennett and Remi Denton. 2024. AI’s Regimes of Representation: A Community-Centered Study of Text-to-Image Models in South Asia. https://mit-serc.pubpub.org/pub/bfw5tscj. · DOI
  14. Kasper Rodil and Heike Winschiers-Theophilus. 2018. Why Is She Naked?: An iterative refinement of the digitisation of ICH with the OvaHimba tribe in Namibia. International journal of intangible heritage 13 (2018) 143–154.
  15. 10.1145/2399016.2399111 · DOI
  16. Akshit Singh. 2024. Diverse Yet Biased: Towards Mitigating Biases in Generative AI (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence 38 21 (Mar. 2024) 23653–23654. 10.1609/aaai.v38i21.30512 · DOI
  17. Maria Skublewska-Paszkowska Marek Milosz Pawel Powroznik and Edyta Lukasik. 2022. 3D technologies for intangible cultural heritage preservation—literature review for selected databases. Heritage Science 10 1 (2022) 3. · DOI

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Metadata

Authors
Auala, S., Mbinge, U., Ndjao, L., van Niekerk, K. C., Nathingo, N., Nehoya, R., Smith, R. C., Rodil, K., Kapuire, G. K., Maasz, D., & Winschiers-Theophilus, H.
Affiliations
  • Namibia University of Science and Technology (NA)
  • Otjisa Community, Otjisa, Kunene, Namibia
  • Aarhus University (DK)
  • Aalborg University (DK)
  • Namibia University of Science and Technology, Windhoek, Khomas, Namibia
ORCID
Venue
Proceedings of the Fifth Biennial African Human-Computer Interaction Conference
Publisher
ACM
Published
2025-11-04
Pages
358-368
DOI
10.1145/3757232.3757268
OpenAlex
W4415870563
Cited by
2
References
1
Citations by year
2026: 2
FWCI
2.06
Type
conference paper
Language
en
Open access
Licence
cc-by-nc-sa
Locations
  • African Human Computer Interaction Conference (AfriCHI) (publishedVersion, cc-by-nc-sa)
  • VBN Forskningsportal (Aalborg Universitet) (publishedVersion, cc-by-nc-sa)
Funders
  • MTC Namibia
  • Aarhus Universitet
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
responsible AI, cultural validity, Ovahimba, indigenous knowledge, cultural heritage, heritage futures

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