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Cited by 11Ethics and Social Impacts of AIIndigenous Health, Education, and RightsE-Government and Public Services

Tribal Knowledge Cocreation in Generative Artificial Intelligence Systems

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Wang, Yi-Fan, Chen, Yu-Che, Huang, Yen-Chen, Redwing, Carol, Tsai, Chun-Hua·2024·Proceedings of the 25th Annual International Conference on Digital Government Research
DOI 10.1145/3657054.3657129
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Abstract

Generative Artificial Intelligence (AI) systems bring innovative ways of information provision and knowledge delivery. In the public sector, generative AI has the potential to decrease bureaucratic discretion in the decision-making process. Increasing reliance on this technology brings challenges of unfair treatment, colonized responses from the system, and data governance. Because of historical interaction, tribal communities are the most underrepresented in policy planning and implementation. Indigenous communities suffer from the neglect of tribal sovereignty by the U.S. federal government and limited accessibility and literacy in the digital world. Generative AI systems exacerbate these challenges with insufficient tribal input. However, the negative impact can be alleviated with digital equity and knowledge cocreation. Digital equity emphasizes the importance of tribal knowledge representation, and knowledge cocreation focuses on the collaboration between Indigenous communities and relevant actors in data governance for generative AI systems. This study proposes two research questions to discuss tribal knowledge cocreation in generative AI systems: (1) what are the biases in the system responses from the tribal perspective? (2) what are the potential resolutions for these problems? The findings from in-depth interviews with tribal members in the U.S. indicate that the insufficient articulation of tribal culture, the lack of crucial tribal historical events, and the inappropriate appellation of tribal nations are the primary drawbacks in the system responses. From the Indigenous perspective, tribal oral traditions, native publications and documents, and collaboration with tribal governments can address the problems of generative AI responses. This study contributes to the theory development of digital equity and knowledge cocreation in tribal generative AI system responses. Policy recommendations and future research agendas are included in this research.

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  • In the U.S. tribal participatory design project, the process relied on pre-trained, non-Indigenous platforms whose datasets, design logics, and governance remained outside community control.

    “Although framed as co-creation, the process relied on pre-trained, non-Indigenous platforms whose datasets, design logics, and governance remained outside community control (Wang et al., 2024).”

    From Towards a decolonial AI sovereignty model for indigenous educational settings (2026) · cites this paper as Wang et al., 2024

    The Interview Processes passage describes U.S. tribal members reviewing an existing generative-AI chatbot’s responses, and the Discussion says those responses lacked tribal input and reflected non-tribal perspectives. The paper does not describe a participatory platform-design project or establish that the platform’s training datasets, design logic, and governance remained outside community control. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Brazilian educators localised outputs for ecological narratives; U.S. tribal co-design sessions exposed stereotyped imagery in generative models but lacked authority to correct underlying systems.

    “Brazilian educators localised outputs for ecological narratives; U.S. tribal co-design sessions exposed stereotyped imagery in generative models but lacked authority to correct underlying systems (Wang et al., 2024).”

    From Towards a decolonial AI sovereignty model for indigenous educational settings (2026) · cites this paper as Wang et al., 2024

    The Preliminary Findings (Interview Processes; page unavailable) describe U.S. tribal members reviewing chatbot responses in interviews and identifying biased or missing information. The Discussion describes colonized perspectives and disrespectful tribal names. Our copy does not describe Brazilian educators, ecological narratives, stereotyped imagery, co-design sessions, or participants lacking authority to change the underlying systems. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Participants in the U.S. tribal project identified stereotyped, inaccurate, and missing representations, demonstrating that even the promise of inclusion can be hollow without control over the underlying data infrastructure.

    “Participants identified stereotyped, inaccurate, and missing representations, demonstrating what Couldry and Mejias argue: even the promise of inclusion can be hollow without control over the underlying data infrastructure (2019).”

    From Towards a decolonial AI sovereignty model for indigenous educational settings (2026) · cites this paper as Wang et al., 2024

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Authors
Wang, Y.-F., Chen, Y.-C., Huang, Y.-C., Redwing, C., & Tsai, C.-H.
Affiliations
  • National Taiwan University (TW)
  • University of Nebraska at Omaha (US)
  • Purdue University West Lafayette (US)
ORCID
Venue
Proceedings of the 25th Annual International Conference on Digital Government Research
Publisher
ACM
Published
2024-06-11
Pages
637-644
ISSN
3050-8681 (ISSN-L)
DOI
10.1145/3657054.3657129
OpenAlex
W4399254954
Cited by
11
References
4
Citations by year
2026: 6 · 2025: 5
FWCI
5.01
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • Annual International Conference on Digital Government Research (publishedVersion)
  • DigitalCommons@UNO (submittedVersion)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Health › Indigenous Health, Education, and Rights
  • Social Sciences › Political Science and International Relations › E-Government and Public Services
Keywords
generative AI, tribal knowledge, indigenous peoples, digital equity, data governance, algorithmic bias, tribal sovereignty, tribal nations, oral tradition, public sector decision-making

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