The Postdigital Challenge of Critical Media Literacy

In: The International Journal of Critical Media Literacy

This article situates contemporary critical media literacy into a postdigital context. It examines recent advances in data literacy, with an accent to Big Data literacy and data bias, and expands them with insights from critical algorithm studies and the critical posthumanist perspective to education. The article briefly outlines differences between older software technologies and artificial intelligence (AI), and introduces associated concepts such as machine learning, neural networks, deep learning, and AI bias. Finally, it explores the complex interplay between Big Data and AI and teases out three urgent challenges for postdigital critical media literacy. (1) Critical media literacy needs to reinvent existing theories and practices for the postdigital context. (2) Reinvented theories and practices need to find a new balance between the technological aspects of data and AI literacy with the political aspects of data and AI literacy, and learn how to deal with non-predictability. (3) Critical media literacy needs to embrace the posthumanist challenge; we also need to start thinking what makes AIs literate and develop ways of raising literate thinking machines. In our postdigital age, critical media literacy has a crucial role in conceptualisation, development, and understanding of new forms of intelligence we would like to live with in the future.

  • Baeza-Yates R. (2016). Data and algorithmic bias in the web. Proceedings of the 8th acm Conference on Web Science – WebSci ’16. Doi:10.1145/2908131.2908135.

  • Barron C. (2003). A strong distinction between humans and non-humans is no longer required for research purposes: A debate between Bruno Latour and Steve fuller. History of the Human Sciences, 16(2), 77–99.

    • Search Google Scholar
    • Export Citation
  • Braidotti R . (2015). Introduction to a lecture by Luciana Parisi, automated cognition, algorithmic capitalism and the incomputable. http://cfh-lectures.hum.uu.nl/automated-cognition-algorithmic-capitalism-and-the-incomputable/. Accessed 15 October 2018.

    • Search Google Scholar
    • Export Citation
  • Bughin J. ; Hazan E. ; Ramaswamy S. ; Chui M. ; Allas T. ; Dahlström P. ; Henke N. ; & Trench M. (2017). Artificial Intelligence the Next Digital Frontier?. Discussion Paper. McKinsey Global Institute. https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx. Accessed 15 October 2018.

    • Search Google Scholar
    • Export Citation
  • D’Ignazio C. & Bhargav R. (2015). Approaches to Building Big Data Literacy. Bloomberg Data for Good Exchange Conference. 28-Sep-2015, New York City, NY, usa . https://dam-prod.media.mit.edu/x/2016/10/20/Edu_D’Ignazio_52.pdf. Accessed 11 October 2018.

    • Search Google Scholar
    • Export Citation
  • Daly L . (2017). AI Literacy: The basics of machine learning. https://worldwritable.com/ai-literacy-the-basics-of-machine-learning-2e20f93e34b4. Accessed 15 October 2018.

    • Search Google Scholar
    • Export Citation
  • Dastin J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters Business News, 10 October. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. Accessed 11 October 2018.

    • Search Google Scholar
    • Export Citation
  • Dean J. ; Medak T. & Jandrić P. (2018). Embrace the antagonism, build the Party! The new communist horizon in and against communicative capitalism. Postdigital Science and Education, 1(1). Doi: https://doi.org/10.1007/s42438-018-0006-7.

    • Search Google Scholar
    • Export Citation
  • Faggella D . (2017). The Rise of Neural Networks and Deep Learning in Our Everyday Lives – A Conversation with Yoshua Bengio. https://www.techemergence.com/the-rise-of-neural-networks-and-deep-learning-in-our-everyday-lives-a-conversation-with-yoshua-bengio/. Accessed 15 October 2018.

  • Fuchs C. (2011). Foundations of Critical Media and Information Studies. London and New York: Routledge.

  • Fuller S. & Jandrić P. (2018). The Postdigital Human: Making the history of the future. Postdigital Science and Education, 1(1). Doi: https://doi.org/10.1007/s42438-018-0003-x.

    • Export Citation
  • Hempel J. (2018). Want to prove your business is fair? Audit your algorithm. Wired, 5 September. https://www.wired.com/story/want-to-prove-your-business-is-fair-audit-your-algorithm/.

    • Search Google Scholar
    • Export Citation
  • ibm Research (2018). AI and bias. https://www.research.ibm.com/5-in-5/ai-and-bias/. Accessed 15 October 2018.

  • Jandrić P. (2017). Learning in the Age of Digital Reason . Rotterdam: Sense.

  • Jandrić P. ; Knox J. ; Besley T. ; Ryberg T. ; Suoranta J. & Hayes S. (2018). Postdigital Science and Education. Educational Philosophy and Theory . Doi: https://doi.org/10.1080/00131857.2018.1454000.

    • Search Google Scholar
    • Export Citation
  • Jones C. (2018). Experience and Networked Learning. In Bonderup Dohn N. ; Cranmer S. ; Sime J.A. ; M. de Laat & Ryberg T. (Eds.), Networked Learning: Reflections and Challenges, pp. 39–56. Springer International.

    • Search Google Scholar
    • Export Citation
  • Kellner D. & Share J. (2007). Critical media literacy is not an option. Learning Inquiry, 1(1), 59–69. Doi: https://doi.org/10.1007/s11519-007-0004-2.

    • Export Citation
  • Knox J. (2015). Critical education and digital cultures. In Peters M. (Ed.), Encyclopedia of educational philosophy and theory. Singapore: Springer, 1–6. Doi: https://doi.org/10.1007/978-981-287-532-7_124-1.

    • Search Google Scholar
    • Export Citation
  • Koltay T. (2015). Data literacy: in search of a name and identity. Journal of Documentation, 71(2), 401–415. Doi: https://doi.org/10.1108/JD-02-2014-0026.

    • Export Citation
  • Mager A. (2014). Defining algorithmic ideology: Using ideology critique to scrutinize corporate search engines. Triple C: Communication, Capitalism and Critique, 12, 28–39.

    • Search Google Scholar
    • Export Citation
  • Ng A . (2018). Machine Learning. https://www.coursera.org/learn/machine-learning. Accessed 15 October 2018.

  • O’Keeffe C. (2017). Economizing education: Assessment algorithms and calculative agencies. E-Learning and Digital Media, 14(3), 123–137. Doi: https://doi.org/10.1177/2042753017732503.

    • Search Google Scholar
    • Export Citation
  • Peters M. (2017). Deep learning, education and the final stage of automation, Educational Philosophy and Theory, https://www.tandfonline.com/doi/abs/10.1080/00131857.2017.1348928.

    • Search Google Scholar
    • Export Citation
  • Peters M.A. (2012). Algorithmic capitalism and educational futures: Informationalism and the googlization of knowledge. TruthOut. http://truth-out.org/news/item/8887-algorithmic-capitalism-and-educational-futures-informational ism-and-the-googlization-of-knowledge. Accessed 15 October 2018.

  • Peters M.A. & Besley T. (2018). Critical Philosophy of the Postdigital. Postdigital Science and Education. Doi: https://doi.org/10.1007/s42438-018-0004-9.

    • Search Google Scholar
    • Export Citation
  • Peters M.A. , & Bulut E. (Eds). (2011). Cognitive capitalism, education and digital labor . New York, NY: Peter Lang.

  • Peters M.A. & Jandrić P. (2018). The Digital University: A Dialogue and Manifesto . New York: Peter Lang.

  • Peters M.A. & Jandrić, P. (forthcoming, 2019). AI, Human Evolution, and the Speed of Learning. In Knox J. ; Wang Y. ; & Gallagher M. (Eds.), Artificial Intelligence and Inclusive Education: speculative futures and emerging practices. Springer Nature.

    • Search Google Scholar
    • Export Citation
  • Shadbolt N. , Hall W. , Hendler J.A. , & Dutton W.H. (2013). Web science: A new frontier. Philosophical Transactions of the Royal Society A , 371: 20120512.

    • Search Google Scholar
    • Export Citation
  • Williamson B . (2016). Who owns educational theory? Pearson, big data and the ‘theory gap.’ Retrieved 7 July 2017 from https://codeactsineducation.wordpress.com/2016/01/19/who-ownseducational-theory/. Accessed 15 October 2018.

  • Williamson B. (2017). Who owns educational theory? Big data, algorithms and the expert power of education data science. E-Learning and Digital Media, 14(3), 105–122. Doi: https://doi.org/10.1177%2F2042753017731238.

    • Search Google Scholar
    • Export Citation

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