Developing Culturally Responsive AI for Indigenous Knowledge Preservation and Revitalization

Cry Kuranga; Amahle Khumalo; Tlou Maggie Masenya

doi:10.25159/2663-659X/20992

Authors

Cry Kuranga Durban University of Technology https://orcid.org/0000-0002-2801-6974
Amahle Khumalo Durban University of Technology https://orcid.org/0000-0002-2001-9919
Tlou Maggie Masenya Durban University of Technology https://orcid.org/0000-0001-5400-0549

DOI:

https://doi.org/10.25159/2663-659X/20992

Keywords:

algorithmic decolonisation, AI, ethical AI, explainable AI, Indigenous knowledge systems

Abstract

Artificial intelligence (AI) can promote and use Indigenous knowledge systems (IKS). However, if its implementation is not approached thoughtfully, it could pose risks to IKS. The underlying algorithms that drive AI systems may have inherent biases. These algorithmic biases may perpetuate and amplify the existing social inequalities and further marginalise Indigenous communities. This study explores potential risks that processes underlying AI systems may pose to IKS and proposes a framework that promotes transparent and explainable development of AI solutions which align with Indigenous communities’ cultural values and knowledge systems. The proposed framework is rooted in three core principles: epistemic justice, algorithmic transparency and explainability, and community-led design. The framework strives to create AI–IKS systems that promote self-determination within Indigenous communities, thereby dismantling colonial legacies and the dominance of Western ways of knowing. Several techniques to implement these core principles are proposed which include culturally-aware data augmentation and explainable AI. The findings of this study add to the important conversation about ethical and responsible AI development which is instrumental in providing opportunities for equitable and culturally sustainable AI. It is recommended that Indigenous communities should be involved from the outset of such projects.

References

Battiste, M. 2024. Protecting Indigenous Knowledge and Heritage: A Canadian Obligation. Vancouver: UBC Press. https://doi.org/10.59962/9780774880831 DOI: https://doi.org/10.59962/9780774880831

Bench-Capon, T. J. 2014. Knowledge Representation: An Approach to Artificial Intelligence. London: Harcourt Brace Jovanovich Publishers.

Berkes, F. 2018. Sacred Ecology. 4th ed. New York: Routledge. https://doi.org/10.4324/9781315114644 DOI: https://doi.org/10.4324/9781315114644

Bødker, S., C. Dindler, O. S. Iversen, and R. C. Smith. 2022. “What Is Participatory Design?” In Participatory Design, edited by Bødker, S., C. Dindler, O. S. Iversen, and R. C. Smith, 5–13. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-031-02235-7_2 DOI: https://doi.org/10.1007/978-3-031-02235-7_2

Braun, V., and V. Clarke. 2006. “Using Thematic Analysis in Psychology.” Qualitative Research in Psychology 3 (2): 77–101. https://doi.org/10.1191/1478088706qp063oa DOI: https://doi.org/10.1191/1478088706qp063oa

Brown, T., and J. Wyatt. 2015. “Design Thinking for Social Innovation.” Annual Review of Policy Design 3 (1): 1–10. https://ojs.unbc.ca/index.php/design/article/view/1272

Cajete, G., M. K. Nelson, and D. Shilling. 2018. “Native Science and Sustaining Indigenous Communities.” In Traditional Ecological Knowledge: Learning from Indigenous Practices for Environmental Sustainability, edited by M. K. Nelson and D. Shilling, 15–26. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108552998.003 DOI: https://doi.org/10.1017/9781108552998.003

Carroll, S. R., I. Garba, O. L. Figueroa-Rodríguez, J. Holbrook, R. Lovett, S. Materechera, M. Parsons, et al. 2020. “The CARE Principles for Indigenous Data Governance.” Data Science Journal 19: 43. https://doi.org/10.5334/dsj-2020-043 DOI: https://doi.org/10.5334/dsj-2020-043

Couldry, N., and U. Mejias. 2019. “Making Data Colonialism Liveable: How Might Data’s Social Order Be Regulated?” Internet Policy Review 8 (2). https://doi.org/10.14763/2019.2.1411 DOI: https://doi.org/10.14763/2019.2.1411

Crawford, K. 2021. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven, CT: Yale University Press. https://doi.org/10.12987/9780300252392 DOI: https://doi.org/10.12987/9780300252392

Creswell, J. W. 2014. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Thousand Oaks, CA: Sage Publications.

Christen, K. 2012. “Does Information Really Want to Be Free? Indigenous Knowledge Systems and the Question of Openness.” International Journal of Communication 6: 2870–2893. https://ijoc.org/index.php/ijoc/article/view/1618

Doshi-Velez, F., and B. Kim. 2017. “Towards a Rigorous Science of Interpretable Machine Learning.” Preprint, arXiv, February 22, 2017. https://doi.org/10.48550/arXiv.1702.08608

Ehrlinger L., and W. Wöß. 2016. “Towards a Definition of Knowledge Graphs.” Paper presented at SEMANTICS 2016: Posters and Demos Track September 13–14, 2016, Leipzig, Germany. https://ceur-ws.org/Vol-1695/paper4.pdf

Fisher, A., C. Rudin, and F. Dominici. 2019. “All Models Are Wrong, But Many Are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models.” Journal of Machine Learning Research 20 (177): 1–81. http://jmlr.org/papers/v20/18-760.html

Fricker, M. 2007. Epistemic Injustice: Power and the Ethics of Knowing. Oxford: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198237907.001.0001 DOI: https://doi.org/10.1093/acprof:oso/9780198237907.001.0001

Goodman, B., and S. Flaxman. 2017. “European Union Regulations on Algorithmic Decision-Making and a ‘Right to Explanation.’” AI Magazine 38 (3): 50–57. https://doi.org/10.1609/aimag.v38i3.2741 DOI: https://doi.org/10.1609/aimag.v38i3.2741

Kim, G. J. 2015. Human-Computer Interaction: Fundamentals and Practice. New York: CRC Press. https://doi.org/10.1201/b18071 DOI: https://doi.org/10.1201/b18071

Kimmerer, R. W. 2013. Braiding Sweetgrass: Indigenous Wisdom, Scientific Knowledge and the Teachings of Plants. Minneapolis, MN: Milkweed Editions.

Kotsiantis, S. B. 2013. “Decision Trees: A Recent Overview.” Artificial Intelligence Review 39: 261–283. https://doi.org/10.1007/s10462-011-9272-4 DOI: https://doi.org/10.1007/s10462-011-9272-4

Krueger, R. A., and M. A. Casey. 2014. Focus Groups: A Practical Guide for Applied Research. Thousand Oaks, CA: Sage Publications.

Kukutai, T,, and J. Taylor. 2016. “Data Sovereignty for Indigenous Peoples: Current Practice and Future Needs.” In Indigenous Data Sovereignty: Toward an Agenda, edited by T. Kukutai and J. Taylor, 1–22. Canberra: ANU Press. https://doi.org/10.22459/CAEPR38.11.2016 DOI: https://doi.org/10.22459/CAEPR38.11.2016.01

Luckin, R., W. Holmes, M. Griffiths, and L. B. Forcier. 2016. Intelligence Unleashed: An Argument for AI in Education. London: Pearson.

Miller, T. 2019. “Explanation in Artificial Intelligence: Insights from the Social Sciences.” Artificial Intelligence 267: 1–38. https://doi.org/10.1016/j.artint.2018.07.007 DOI: https://doi.org/10.1016/j.artint.2018.07.007

Nakashima, D., K. Galloway McLean, H. D. Thulstrup, A. Ramos Castillo, and J. Rubis. 2012. Weathering Uncertainty: Traditional Knowledge for Climate Change Assessment and Adaptation. Paris: UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000216613

Noble, S. U. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press. https://doi.org/10.18574/nyu/9781479833641.001.0001 DOI: https://doi.org/10.18574/nyu/9781479833641.001.0001

Robertson, T., and I. Wagner. 2012. “Ethnography and Participatory Design.” Communications of the ACM 55 (8): 68–76.

Russell, S. J., and P. Norvig. 2016. Artificial Intelligence: A Modern Approach. Malaysia: Pearson Education.

Selbst, A. D., D. Boyd, S. A. Friedler, S. Venkatasubramanian, and J. Vertesi. 2019. “Fairness and Abstraction in Sociotechnical Systems.” Paper presented at the Proceedings of the Conference on Fairness, Accountability, and Transparency. Atlanta, GA, USA, Association for Computing Machinery, 59–68. https://doi.org/10.1145/3287560.3287598 DOI: https://doi.org/10.1145/3287560.3287598

Shorten, C., and T. M. Khoshgoftaar. 2019. “A Survey on Image Data Augmentation for Deep Learning.” Journal of Big Data 6 (1): 60. https://doi.org/10.1186/s40537-019-0197-0 DOI: https://doi.org/10.1186/s40537-019-0197-0

Smith, L. T. 2021. Decolonizing Methodologies: Research and Indigenous Peoples. 3rd ed. London: Bloomsbury Publishing.

Wachter, S., B. D. Mittelstadt, and C. Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harvard Journal of Law and Technology 31 (2): 841–887. https://doi.org/10.2139/ssrn.3063289 DOI: https://doi.org/10.2139/ssrn.3063289

Walter, M., and M. Suina. 2019. “Indigenous Data Sovereignty: Toward an Agenda.” International Journal of Social Research Methodology 22 (3) 233–243. https://doi.org/10.1080/13645579.2018.1531228 DOI: https://doi.org/10.1080/13645579.2018.1531228