Have a personal or library account? Click to login
Māori Algorithmic Sovereignty: Idea, Principles, and Use Cover

Māori Algorithmic Sovereignty: Idea, Principles, and Use

Open Access
|Apr 2024

References

  1. Angwin, J, Larson, J, Mattu, S and Kirchner, L. 2016. Machine Bias. Propublica, 23 May. Available at http://propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
  2. Bartley, N, Abeliuk, A, Ferrera, E and Lerman, K. 2021. Auditing algorithmic bias on Twitter. In: Websci ’21: Proceedings of the 13th ACM Web Science Conference, Stuttgart, Germany on 21–25 June 2021, pp. 6573. DOI: 10.1145/3447535.3462491
  3. Beller, J. 2018. The message is murder: Substrates of computational capital. London: Pluto Press. DOI: 10.2307/j.ctt1x07z9t
  4. Bender, EM, Gebru, T, McMillan-Major, A and Shmitchell, S. 2021. On the dangers of stochastic parrots: Can language models be too big? In: FAccT ‘21: Proceedings of the 2021 ACM Conference on Fairness, Accountability and Transparency, Rio de Janeiro, Brazil on 3–10 March 2021, pp. 610623. DOI: 10.1145/3442188.3445922
  5. Benjamin, R. 2019. Race after technology: Abolitionist tools for the New Jim Code. London: Polity. DOI: 10.1093/sf/soz162
  6. Berardi, F. 2009. The soul at work. Cambridge: MIT Press.
  7. Boscarino, N, Cartwright, RA, Fox, K and Tsosie, KS. 2022. Federated learning and Indigenous genomic data sovereignty. Nature Machine Intelligence, 4(11): 909911. DOI: 10.1038/s42256-022-00551-y
  8. Brynjolfsson, E and McAfee, A. 2014. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York: WW Norton.
  9. Buolamwini, J and Gebru, T. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81: 7791.
  10. Carroll, SR, Garba, I, Figueroa-Rodriguez, OL, Holbrook, J, Lovett, R, Materechera, S, Parsons, M, Raseroka, K, Rodriguez-Lonebear, D, Rowe, R, Sara, R, Walker, JD, Anderson, J and Hudson, M. 2020. The care principles for indigenous data governance. Data Science Journal, 19(4): 112. DOI: 10.5334/dsj-2020-043
  11. Checketts, L. 2022. Artificial intelligence and the marginalisation of the poor. Journal of Moral Theology, 11(1): 87111. DOI: 10.55476/001c.34125
  12. Dewes, TK. 2017. He taonga ranei tēnei mea te raraunga – How is data a taonga? Report submitted for MAOR591 Directed Study. Hamilton, NZ: University of Waikato.
  13. Dourish, P. 2016. Algorithms and their others: Algorithmic culture in context. Big Data and Society, 3(2): 111. DOI: 10.1177/2053951716665128
  14. Dressel, J and Farid, H. 2018. The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4(1): 15. DOI: 10.1126/sciadv.aao5580
  15. Grother, P, Ngan, M and Hanaoka, K. 2019. Face recognition vendor test (FRVT) Part 3: Demographic effects. National Institute of Standards and Technology. DOI: 10.6028/NIST.IR.8280
  16. Henderson, P, Hu, J, Romoff, J, Brunskill, E, Jurafsky, D and Pineau, J. 2020. Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research, 21(248): 143.
  17. Hudson, ML, Anderson, T, Dewes, TK, Temara, P, Whaanga, H and Roa, T. 2017. He Matapihi ki te Mana Raraunga: Conceptualising Big Data through a Māori lens. In: Whaanga, H, Keegan, TTAG and Apperley, M (eds.), He Whare Hangarau Māori – Language, culture & technology. pp. 6473. Hamilton, New Zealand: Te Pua Wānanga ki te Ao / Faculty of Māori and Indigenous Studies, the University of Waikato.
  18. Hudson, ML and Russel, K. 2009. The Treaty of Waitangi and research ethics in Aotearoa. Bioethical Inquiry, 6(1): 6168. DOI: 10.1007/s11673-008-9127-0
  19. Huws, U. 2014. Labor in the Growing Digital Economy: The cybertariat comes of age. New York: NYU Press.
  20. Jackson, M. 2019. In the end “the hope of decolonisation”. In: McKinley, A and Smith, LT (eds.), Handbook of Indigenous Education. Springer Nature. pp. 101110. DOI: 10.1007/978-981-10-3899-0_59
  21. Jobin, A, Ienca, M and Vayena, E. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9): 389399. DOI: 10.1038/s42256-019-0088-2
  22. Kordzadeh, N and Ghasemaghaei, M. 2022. Algorithmic bias: Review, synthesis, and future research directions. European Journal of Information Systems, 31(3): 388409. DOI: 10.1080/0960085X.2021.1927212
  23. Kukutai, T and Taylor, J. (eds.) 2016a. Indigenous data sovereignty: Toward an agenda, Vol. 38. Canberra, AU: ANU Press. http://www.jstor.org/stable/j.ctt1q1crgf. DOI: 10.22459/CAEPR38.11.2016
  24. Kukutai, T and Taylor, J. 2016b. Data sovereignty for indigenous peoples: Current practice and future needs. In: Kukutai, T and Taylor, J (eds.), Indigenous Data Sovereignty: Toward and Agenda, Vol. 38. Canberra, AU: ANU Press. DOI: 10.22459/CAEPR38.11.2016.01
  25. Kukutai, T, Cassim, S, Clark, V, Jones, N, Mika, J, Morar, R, Muru-Lanning, M, Pouwhare, R, Teague, V, Tuffery Huria, L, Watts, D and Sterling, R. 2023a. Māori data sovereignty and privacy. Tikanga in Technology discussion paper. Hamilton: Te Ngira Institute for Population Research.
  26. Kukutai, T, Campbell-Kamariera, K, Mead, A, Mikaere, K, Moses, C, Whitehead, J and Cormack, D. 2023b. Māori data Governance model. Te Kāhui Raraunga.
  27. Lambrecht, A and Tucker, C. 2019. Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7): 29662981. DOI: 10.1287/mnsc.2018.3093
  28. Lamdan, S. 2022. Data Cartels: The Companies that Control and Monopolize Our Information. Stanford, CA: Stanford University Press. DOI: 10.1515/9781503633728
  29. Mackey, TK, Calac, AJ, Chenna Keshava, BS, Yracheta, J, Tsosie, KS and Fox, K. 2022. Establishing a blockchain-enabled Indigenous data sovereignty framework for genomic data. Cell, 185(15): 26262631. PMID: 35868267. DOI: 10.1016/j.cell.2022.06.030
  30. Martel, R, Shepherd, M and Goodyear-Smith, F. 2022. He awa whiria – A braided river: An indigenous Māori approach to mixed methods research. Journal of Mixed Methods Research, 16(1): 1733. DOI: 10.1177/1558689820984028
  31. Martin, K. 2019. Designing ethical algorithms. MIS Quarterly Executive, 18(5): 2. DOI: 10.17705/2msqe.00012
  32. Meijas, UA and Couldry, N. 2019. The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford, CA: Stanford University Press. DOI: 10.1515/9781503609754
  33. Milne, BJ, Atkinson, J, Blakely, T, Day, H, Douwes, J, Gibb, S, Nicholson, M, Shackleton, N, Sporle, A, and Teng, A. 2019. Data resource profile: The New Zealand integrated data infrastructure (IDI). International Journal of Epidemiology, 48(3): 677677e. DOI: 10.1093/ije/dyz014
  34. Moewaka Barnes, H and McCreanor, T. 2019. Colonisation, Hauora, and whenua in Aotearoa. Journal of the Royal Society of New Zealand, 49(1): 1933. DOI: 10.1080/03036758.2019.1668439
  35. Munn, L. 2017. I am a driver-partner. Work, Organisation, Labour and Globalisation, 11(2): 720. DOI: 10.13169/workorgalaboglob.11.2.0007
  36. Munn, L. 2023. The five tests: designing and evaluating AI according to indigenous Māori principles. AI & Society. DOI: 10.1007/s00146-023-01636-x
  37. O’Neil, C. 2016. Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
  38. Olhede, SC and Wolfe, PJ. 2018. The growing ubiquity of algorithms in society: Implications, impacts and innovations. Philosophical Transactions of the Royal Society A, 376(1): 20170364. DOI: 10.1098/rsta.2017.0364
  39. Pool, I. 2016. Colonialism’s and postcolonialism’s fellow traveller: The collection, use and misuse of data on indigenous people. In: Kukutai, T and Taylor, J (eds.), Indigenous Data Sovereignty: Toward an Agenda. ANU Press. pp 5778. DOI: 10.22459/CAEPR38.11.2016.04
  40. Rainie, SC, Kukutai, T, Walter, M, Figueroa-Rodriguez, OL, Walker, J and Axelsson, P. 2019. Issues in open data: Indigenous data sovereignty. In: Davies, T, Walker, S, Rubinstein, M and Perini, F (eds.), The State of Open Data: Histories and Horizons. Cape Town and Ottawa: African Minds and International Development Research Centre. pp. 300319.
  41. Smith, LT. 2012. Decolonising Methodologies: Research and Indigenous Peoples. 2nd Ed. Zed Books Ltd.
  42. Someh, I, Davern, M, Breidbach, CF and Shanks, G. 2019. Ethical issues in big data analytics: A stakeholder perspective. Communications of the Association of Information Systems, 44(1): 34. DOI: 10.17705/1CAIS.04434
  43. Stats, NZ. 2018. Algorithm Assessment Report. Available from https://data.govt.nz/use-data/analyse-data/government-algorithm-transparency.
  44. Stats, NZ. 2020a. Ngā Tikanga Paihere: a framework guiding ethical and culturally appropriate data use. Available from https://data.govt.nz/toolkit/data-ethics/nga-tikanga-paihere/.
  45. Stats, NZ. 2020b. Algorithm Charter for Aotearoa New Zealand. Available from https://data.govt.nz/assets/data-ethics/algorithm/Algorithm-Charter-2020_Final-English-1.pdf.
  46. Strubell, E, Ganesh, A and MacCallum, A. 2019. Energy and policy considerations for deep learning In: NLP. Proceedings of the 57th Annual Meeting of the Association of Computational Linguistics, pp. 36453650. DOI: 10.18653/v1/P19-1355
  47. Taylor Fry. 2021. Algorithm Charter for Aotearoa New Zealand: Year 1 review. Available at https://www.data.govt.nz/assets/data-ethics/algorithm/Algorithm-Charter-Year-1-Review-FINAL.pdf.
  48. Te Mana Raraunga. 2016. Te Mana Raraunga Māori Data Sovereignty Network Charter. Available at https://static1.squarespace.com/static/58e9b10f9de4bb8d1fb5ebbc/t/5bda208b4ae237cd89ee16e9/1541021836126/TMR+Ma%CC%84ori+Data+Sovereignty+Principles+Oct+2018.pdf.
  49. Waitangi Tribunal. 2011. Ko Aotearoa Tēnei (WAI 262). Wellington, NZ: Waitangi Tribunal. Available at https://forms.justice.govt.nz/search/WT/reports/reportSummary.html.
  50. Waitangi Tribunal. 2016. Report on the Trans-Pacific Partnership Agreement. Wellington, NZ: Waitangi Tribunal.
  51. Waitangi Tribunal. 2019. Hauora – Report on Stage One of the Health Services and Outcomes Kaupapa Inquiry (WAI 2575). Wellington, NZ: Waitangi Tribunal. pp 163164.
  52. Walter, M and Anderson, C. 2013. Indigenous Statistics: A Quantitative Research Methodology. Left Coast Press, California.
  53. Walter, M and Kukutai, T. 2018. Artificial intelligence and Indigenous data sovereignty. Input paper for the Horizon Scanning Project, “The Effective and Ethical Development of Artificial Intelligence: An Opportunity to Improve Our Wellbeing”. Australian Council of Learned Academies. Available at www.acola.org.
  54. Walter, M and Suina, M. 2019 Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology, 22(3): 233243. DOI: 10.1080/13645579.2018.1531228
  55. Waziyatawin and Yellow Bird, M. 2005. For Indigenous Eyes Only: A Decolonization Handbook 1. New Mexico, AZ: SAR Press.
  56. West, K, Wilson, D, Thompson, A and Hudson, M. 2020. Māori perspectives on trust and automated decision-making. Report for the Digital Council Aotearoa New Zealand.
  57. Will, P, Krpan, D and Lordan, G. 2023. People versus machines: introducing the HIRE framework. Artificial Intelligence Review, 56(1): 10711100. DOI: 10.1007/s10462-022-10193-6
  58. Yesiler, F, Marius, M, Serra, J and Gomez, E. 2022. Addressing algorithmic biases for musical version identification. In: WSDM ’22: Proceedings of the 15th ACM International Conference on Web Search and Data Mining, pp. 12841290. DOI: 10.1145/3488560.3498397
Language: English
Submitted on: Oct 9, 2023
|
Accepted on: Mar 7, 2024
|
Published on: Apr 2, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Paul T. Brown, Daniel Wilson, Kiri West, Kirita-Rose Escott, Kiya Basabas, Ben Ritchie, Danielle Lucas, Ivy Taia, Natalie Kusabs, Te Taka Keegan, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.