Article Sidebar

Submitted
May 3, 2022
Accepted
Nov 14, 2022
Published
Dec 30, 2022
Download
PDF
Statistic
Read Counter : 683 Download : 502

Main Article Content

Abstract

This paper explores digital marginalization, data marginalization, and algorithmic exclusions in the Souths. To this effect, it argues that underrepresented users and communities continue to be marginalized and excluded by digital technologies, by big data, and by algorithms employed by organizations, corporations, institutions, and governments in various data jurisdictions. Situating data colonialism within the Souths, the paper contends that data ableism, data disablism, and data colonialism are at play when data collected, collated, captured, configured, and processed from underrepresented users and communities is utilized by mega entities for their own multiple purposes. It also maintains that data coloniality, as opposed to data colonialism, is impervious to legal and legislative interventions within data jurisdictions. Additionally, it discusses digital citizenship (DC) and its related emerging regimes. Moreover, the paper argues that digital exclusion transcends the simplistic haves versus the have nots dualism as it manifests itself in multiple layers and in multiple dimensions. Furthermore, it characterizes how algorithmic exclusions tend to perpetuate historical human biases despite the pervasive view that algorithms are autonomous, neutral, rational, objective, fair, unbiased, and non-human. Finally, the paper advances a critical southern decolonial (CSD) approach to datafication, algorithms, and digital citizenship by means of which data coloniality, algorithmic coloniality, and the coloniality embodied in DC have to be critiqued, challenged, and dismantled.

Keywords

Digital Citizenship Digital Marginalization Data Marginalization Algorithmic Exclusions Data Colonialism Critical Southern Decoloniality

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The author declares that the submitted to Journal of e-Learning and Knowledge Society (Je-LKS) is original and that is has neither been published previously nor is currently being considered for publication elsewhere.
The author agrees that SIe-L (Italian Society of e-Learning) has the right to publish the material sent for inclusion in the journal Je-LKS.
The author agree that articles may be published in digital format (on the Internet or on any digital support and media) and in printed format, including future re-editions, in any language and in any license including proprietary licenses, creative commons license or open access license. SIe-L may also use parts of the work to advertise and promote the publication.
The author declares s/he has all the necessary rights to authorize the editor and SIe-L to publish the work.
The author assures that the publication of the work in no way infringes the rights of third parties, nor violates any penal norms and absolves SIe-L from all damages and costs which may result from publication.

The author declares further s/he has received written permission without limits of time, territory, or language from the rights holders for the free use of all images and parts of works still covered by copyright, without any cost or expenses to SIe-L.

For all the information please check the Ethical Code of Je-LKS, available at http://www.je-lks.org/index.php/ethical-code

How to Cite
Chaka, C. (2022). Digital marginalization, data marginalization, and algorithmic exclusions: a critical southern decolonial approach to datafication, algorithms, and digital citizenship from the Souths. Journal of E-Learning and Knowledge Society, 18(3), 83-95. https://doi.org/10.20368/1971-8829/1135678

References

  1. Adams, R. (2021). Can artificial intelligence be decolonized? Interdisciplinary Science Reviews, 46(1–2), 176–197. https://doi.org/10.1080/03080188.2020.1840225
  2. Ali, s. M. (2017). Decolonizing information narratives: Entangled apocalyptics, algorithmic racism and the myths of history. Proceedings, 1(50), 1–3. https://doi.org/10.3390/IS4SI-2017-03910
  3. Andrejevic, M. (2014). Big data, big questions: The big data divide. Journal of International Communication, 8, 1673–89.
  4. Armillas-Tiseyra, M., & Mahler, A. G. (2021). Introduction: New critical directions in Global South Studies. Comparative Literature Studies, 58(3), 465–484. doi: 10.5325/complitstudies.58.3.0465
  5. Arora, P. (2018). Decolonizing privacy studies. Television & New Media, 1–13. https://doi.org/10.1177/1527476418806092
  6. Benabdallah, L., Murillo-Zamora, C., & Adetula, V. (2017). Global south perspectives on international relations theory. https://www.e-ir.info/2017/11/19/global-south-perspectives-on-international-relations-theory/
  7. Birhane, A. (2020). Algorithmic colonization of Africa. SCRIPTed, 17(2), 389–409.
  8. Bloomberg, J. (2018). Digitization, digitalization, and digital transformation: Confuse them at your peril. https://www.forbes.com/sites/jasonbloomberg/2018/04/29/digitization-digitalization-and-digital-transformation-confuse-them-at-your-peril/?sh=53855bce2f2c
  9. Borgesius, F. Z. (2018). Discrimination, artificial intelligence, and algorithmic. https://rm.coe.int/discrimination-artificial-intelligence-and-algorithmic-decision-making/1680925d73
  10. Cahan, E. M., Hernandez-Boussard, T., Thadaney-Isran, S., Rubin, D. L. (2019). Putting the data before the algorithm in big data addressing personalized healthcare. NPJ Digital Medicine, 2(78), 1–6. https://doi.org/10.1038/s41746-019-0157-2
  11. Calzada, I. (2022). Emerging digital citizenship regimes: Pandemic, algorithmic, liquid, metropolitan, and stateless citizenships. Citizenship Studies. https://doi.org/10.1080/13621025.2021.2012312
  12. Carmi, E., Yates, S. J., Lockley, E., & Pawluczuk, A. (2020). Data citizenship: Rethinking data literacy in the age of disinformation, misinformation, and malinformation. Internet Policy Review, 9(2). https://doi.org/10.14763/2020.2.1481
  13. Cave, S., & Dihal, K. (20202). The Whiteness of AI. Philosophy & Technology, 33, 685–703. https://doi.org/10.1007/s13347-020-00415-6
  14. Chaka, C. (2019). Re-imagining literacies and literacies pedagogy in the context of semio-technologies. Nordic Journal of Digital Literacy, 14(1-2), 53–68. https://doi.org/10.18261/issn.1891-943x-2019-0102-05
  15. Chaka, C. (2020). Translanguaging, decoloniality, and the Global South: An integrative review study. Scrutiny 2, 25(1), 6–42. https://doi.10.1080/18125441.2020.1802617
  16. Chaka, C. (2021). Educational data mining, student academic performance prediction, prediction methods, algorithms and tools: An overview of reviews. https://www.preprints.org/manuscript/202108.0345/v1/download
  17. Chaka, C. (2022). The geopolitics of knowledge production in applied English language studies: Transknowledging and a two-eyed critical southern decoloniality (An inaugural lecture delivered on 22 August 2022 at the University of South Africa). https://youtu.be/xb9seQEUR3M OR https://www.youtube.com/watch?v=qu7zUH9lAbE&list=PL4cHAn5OGG5NXSagMU_wiCDczpQOUaCym&index=1
  18. Charitsis, V., & Lehtiniemi, T. (2022). Data ableism: Ability expectations and marginalization in automated societies. Television & New Media, 1–16. https://doi.org/10.1177/15274764221077660
  19. Clarke, M. (2018). Global South: What does it mean and why use the term? https://onlineacademiccommunity.uvic.ca/globalsouthpolitics/2018/08/08/global-south-what-does-it-mean-and-why-use-the-term/
  20. Cofone, I. N. (2019). Algorithmic discrimination is an information problem. Hastings Law Journal, 70(6), 1389–1444. https://repository.uchastings.edu/hastings_law_journal/vol70/iss6/1
  21. Couldry, N., & Mejias, U. A. (2019a). Data colonialism: Rethinking big data’s relation to the contemporary subject. Television & New Media, 20(4), 336–349. https://doi.org/10.1177/1527476418796632
  22. Couldry, N., & Mejias, U. A. (2019b). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford: Stanford University Press.
  23. Couldry, N., & Mejias, U. A. (2021). The decolonial turn in data and technology research: What is at stake and where is it heading? Information, Communication & Society, 1–18. https://doi.org/10.1080/1369118X.2021.1986102
  24. Dados, N., & Connell, R. (2012). The Global South. Contexts, 11(1), 12–13. https://doi.org/10.1177/1536504212436479
  25. Escobar, A. (2007). Worlds and knowledges otherwise: The Latin American modernity/coloniality research program. Cultural Studies, 21(2-3), 179–210.
  26. Floridi, L. (2020). The fight for digital sovereignty: What it is, and why it matters, especially for the EU. Philosophy & Technology, 33(3), 369–378. https://doi.org/10.1007/s13347-020-00423-6
  27. Francis, A. R. (2021). Global Southerners in the North. Temple Law Review, 93, 689–711.
  28. Fund, S. (2022). Digitality vs digitisation: New thinking, new opportunities. https://www.canon-europe.com/business/insights/articles/digitality-digitisation-mind-set/
  29. Gangadharan, S. (2021). Digital exclusion: A politics of refusal. In L. Bernholz, H. Landemore & R. Reich. (Ed.), Digital technology and democratic (pp. 113–140). Chicago: University of Chicago Press. https://doi.org/10.7208/9780226748603-005
  30. Gangadharan, S. P., & and Niklas, J. (2019). Decentering technology in discourse on discrimination. Information, Communication & Society, 22(7), 882–899. https://doi.org/10.1080/1369118X.2019.1593484
  31. Geeker, A., & Hind, S. (2019). Infrastructural surveillance. New Media & Society, 2(8), 1414–1436. https://doi.org/10.1177/1461444819879426
  32. Gilman, M., & Green, R. (2018). The surveillance gap: The harms of extreme privacy and data marginalization. New York University Review of Law & Social Change, 42(253), 253–307.
  33. Gray, J., Gerlitz, C., & Bounegru, L. (2018). Data infrastructure literacy. Big Data & Society, 5(2). https://doi.org/10.1177/2053951718786316
  34. Greenwood, F. (2020). Data colonialism, surveillance capitalism and drones. In D. Specht (Ed.), Mapping crisis: Participation, datafication and humanitarianism in the age of digital mapping (pp. 89–117). London: University of London Press.
  35. Grosfoguel, R. (2007). The epistemic decolonial turn. Cultural Studies. 21(2-3), 211–223.
  36. Heeks, R., & Shekhar, S. (2019). Datafication, development and marginalised urban communities: An applied data justice framework. Information, Communication & Society, 22(7), 992–1011. https://doi.org/10.1080/1369118X.2019.1599039
  37. Hintz, A., Dencik, L., & Wahl-Jorgensen, K. (2019). Digital citizenship in a datafied society. Cambridge, UK.: Polity Press.
  38. Hsu, F. (2017). Resisting the coloniality of English: A research review of strategies. The CATESOL Journal, 29(1), 111–132.
  39. Jago, A. S., & Laurin, K. (2022). Assumptions about algorithms’ capacity for discrimination. Personality and Social Psychology Bulletin, 48(4), 582–595. https://doi.org/10.1177/01461672211016187
  40. Janssen, M., & Kuk, G. (2016). The challenges and limits of big data algorithms in technocratic governance. Government Information Quarterly, 3, 371–377. http://dx.doi.org/10.1016/j.giq.2016.08.011
  41. Karumbaiah, S., & Brooks, J. (2021). How colonial continuities underlie algorithmic injustices in education. IEEE Research in Equity and Sustained Participation in Engineering, Computing, and Technology (IEEE RESPECT). https://doi.org/10.1109/RESPECT51740.2021.9620605
  42. Kloß, S. T. (2017). The Global South as subversive practice: Challenges and potentials of a heuristic concept. The Global South, 11(2), 1–17. https://doi.org/10.2979/globalsouth.11.2.01
  43. Kowalski, A. M. (2020). Global South-Global North differences. In F. W. Leal, A. M. Azul, L. Brandli L., P. Özuyar & T. Wall (Eds.), No poverty, encyclopedia of the UN sustainable development goals (pp. 1–13). https://doi.org/10.1007/978-3-319-69625-6_68-1
  44. Lazar, M. M. (2020). “Politics of the South”: Discourses and praxis. Discourse & Society, 31(1), 5–18. https://doi.org/10.1177/0957926519886126
  45. Lee, N. T., Resnick, P., & Barton, G. (2019). Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms. https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/
  46. Lerman, J. (2013). Big data and its exclusions. Stanford Law Review Online, 66(55), 55–63.
  47. Madden, M., Gilman, M., Levy, K., & Marwick, a. (2017). Privacy, poverty, and big data: A matrix of vulnerabilities for poor Americans. Washington University Law Review, 95(053), 53–125.
  48. Mahler, A. G. (2017). Global South. In E. O’Brien (Ed.), Oxford bibliographies in literary and critical theory. New York: Oxford University Press. doi: https://doi.org/1093/OBO/9780190221911-0055
  49. Maldonado-Torres, N. (2007). On the coloniality of being. Cultural Studies, 21(2), 240–270. http://dx.doi.org/10.1080/09502380601162548
  50. Maldonado-Torres, N. (2018). Outline of ten theses on coloniality and decoloniality. http://fondation-frantzfanon.com/wp-content/uploads/2018/10/maldonado-torres_outline_of_ten_theses-10.23.16.pdf
  51. Martin, C., Hope, S., Zubairi, S. (2016). The role of digital exclusion in social exclusion. https://d1ssu070pg2v9i.cloudfront.net/pex/carnegie_uk_trust/2016/09/LOW-2697-CUKT-Digital-Participation-Report-REVISE.pdf
  52. Mattiuzzo, M. (2019). Algorithms and big data: Considerations on algorithmic governance and its consequences for antitrust analysis. Revista de Economia Contemporânea, 23(2), 1–19. http://dx.doi.org/10.1590/198055272328
  53. Mignolo, W. (2007). Delinking: The rhetoric of modernity, the logic of coloniality and the grammar of de-coloniality. Cultural Studies, 21(2), 449–514. http://dx.doi.org/10.1080/09502380601162647
  54. Milan, S., & Treré, E. (2019). Big data from the South(s): Beyond data universalism. Television & New Media, 20(4), 319–335. https://doi.org/10.1177/1527476419837739
  55. Milan, S., & Treré, E. (2021). Big data from the South(s): An analytical matrix to investigate data at the margins. In D. Rohlinger & S. Sobieraj (Eds.), The Oxford handbook of sociology and digital media (pp. 1–21). Oxford University Press. https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780197510636.001.0001/oxfordhb-9780197510636-e-7
  56. Mohamed, S., Png, M-T., & Isaac, W. (2020). Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophy & Technology, 33, 659–684. https://doi.org/10.1007/s13347-020-00405-8
  57. Mukhametzyanov, I. (2022). Digital citizenship and the student’s digital footprint: Questions of application, promotion and data protection. In Proceedings of the International Scientific and Practical Conference on Computer and Information Security (INFSEC 2021) (pp. 12–16). https://doi.org/10.5220/0010616600003170
  58. Mumford, D. (2021). Data colonialism: compelling and useful, but whither epistemes? Information, Communication & Society. https://doi.org/10.1080/1369118X.2021.1986103
  59. Ndlangamandla, S., & Chaka, C. (2022). Relocating English studies and SoTL in the Global South: Towards decolonizing English and dismantling the coloniality of language. Journal of Contemporary Issues in Education, 17(2), 39-56.
  60. https://doi.org/10.20355/jcie29495
  61. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York: New York Press.
  62. Núñez-Pardo, A. (2020). Inquiring into the coloniality of knowledge, power, and being in EFL textbooks (Indagar la colonialidad del saber, del ser y del poder en libros de texto de inglés). HOW Journal, 27(2), 113–133. https://doi.org/10.19183/how.27.2.566
  63. Orwat, C. (n.d.). Risks of discrimination through the use of algorithms. https://www.antidiskriminierungsstelle.de/EN/homepage/_documents/download_diskr_risiken_verwendung_von_algorithmen.pdf?__blob=publicationFile&v=1
  64. Pawluczuk, A., Yates, S. J., Carmi, E., Lockley, E., & Wessels, B. (2020). Data citizenship framework: exploring citizens’ data literacy through data thinking, data doing and data participation. https://drive.google.com/file/d/1tDq670KtfYqnxkBovcaWP2GpUBcx0ASh/view
  65. Pötzsch, H. (2019). Critical digital literacy: Technology in education beyond issues of user competence and labour-market qualifications. tripleC, 17(2), 221–240.
  66. Quijano, A., & Ennis, M. (2000). Coloniality of power, Eurocentrism, and Latin America. Nepantla: Views from South, 1(3), 533–580.
  67. Raghuveera, N. & Koch, T. (2020). #GoodTechChoices: Addressing unjust uses of data against marginalized communities. https://www.atlanticcouncil.org/blogs/geotech-cues/goodtechchoices-address-unjust-uses-of-data/
  68. Rainie, L., & Anderson, J. (2017). Code-dependent: Pros and cons of the algorithm age. http://www.pewinternet.org/2017/02/08/code-dependent-pros-and-cons-of-the-algorithm-age
  69. Ricaurte, P. (2019). Data epistemologies, the coloniality of power, and resistance. Television & New Media, 20(4), 350–365. https://doi.org/10.1177/1527476419831640
  70. Richardson, J., & Milovidov, E. (2019). Digital citizenship education handbook. Strasbourg: Council of Europe.
  71. Segura, M. S., & Waisbord, S. (2019). Between data capitalism and data citizenship. Television & New Media, 20(4) 412–419. https://doi.org/10.1177/1527476419834519
  72. Sin, J., Franz, R. M., Munteanu, C., & Neves, B. B. (2021). Digital design marginalization: New perspectives on designing inclusive interfaces. . In Proceedings of the CHI ’21. https://doi.org/10.1145/3411764.3445180
  73. Tenney, M., & Sieber, R. (2016). Data-driven participation: Algorithms, cities, citizens, and corporate control. Urban Planning, 1(2), 101–113. https://doi.org/10.17645/up.v1i2.645
  74. Terp, S-J. (2020). #GoodTechChoices: Addressing past and current racism in tech and data. https://www.atlanticcouncil.org/blogs/geotech-cues/goodtechchoices-address-racism-tech-and-data/
  75. Tomczyńska, W. (2017). Digital exclusion: Definitions, causes, countermeasures. Adeptus, 10, 1–26. https://doi.org/10.11649/a.1503
  76. Treré, E. & Milan, S. (2021). Latin American perspectives on datafication and artificial intelligence: Traditions, interventions, and possibilities. Palabra Clave, 24(3), e2431. https://doi.org/10.5294/pacla.2021.24.3.1
  77. Ulbricht, L., & Yeung, K. (2022). Algorithmic regulation: A maturing concept for investigating regulation of and through algorithms. Regulation & Governance, 16, 3–22. https://doi.org/10.1111/rego.12437
  78. Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveillance & society, 12(2), 197–208.
  79. Wikipedia. (2022). Race and crime in the United States. https://en.wikipedia.org/wiki/Race_and_crime_in_the_United_States#:~:text=According%20to%20the%20FBI%2C%20African,3.1%25%20were%20of%20other%20races
  80. Williams, B. A., Brooks, C. F., & Shmargad, y. (2018). Journal of Information Policy, 8, 78–115.
  81. Wolvers, A., Tappe, O., Salverda, T., & Schwarz, T. (2015). Introduction. Concepts of the global south: voices from around the world. https://kups.ub.uni-koeln.de/6399/1/voices012015_concepts_of_the_global_south.pdf
  82. Zembylas, M. (2021). A decolonial approach to AI in higher education teaching and learning: Strategies for undoing the ethics of digital neocolonialism. Learning, Media and Technology. https://doi.org/10.1080/17439884.2021.2010094
  83. Zuboff. S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. New York: Public Affairs.