The risk of algorithmic injustice for interactive learning environments

Rospigliosi, A. 2021. The risk of algorithmic injustice for interactive learning environments. Interactive Learning Environments. 29 (4), pp. 523-526. https://doi.org/10.1080/10494820.2021.1940485

TitleThe risk of algorithmic injustice for interactive learning environments
TypeJournal article
AuthorsRospigliosi, A.
Abstract

Major changes are taking place in education, and the most pressing of these changes is how the role and scope of artificial intelligence (AI) is increasing. After a very long AI winter the exponential growth of very large datasets brought about by mass participation on online platforms, and the increasing sophistication of machine learning algorithms, mean AI is a realisable option. The combination of big data with powerful means of pattern matching is leading to many more instances where the resulting outputs have a real-world impact on learning environments. For example, recently the UK government used an algorithm to calculate the grades of all schoolchildren when the Covid 19 pandemic disrupted summer exams. This very large-scale application of AI was subject to intense scrutiny by the young students, their parents and teachers and was found wanting. This editorial seeks to review the mission of the Journal of Interactive Learning Environments (ILE) in the context of AI and algorithms. This is a useful endeavour as there is a growing recognition of the potential harms, and risks, of the application of AI. These potential harms range from surveillance capitalism, which facilitates the ever more intrusive and intense targeting of commercial content; to systems of citizen control, which use video surveillance as a means of scrutiny, and even subjugation, of populations. This is at a time when the use of AI for scrutiny and control in learning environments offers enormous opportunities, and many researchers and practitioners are exploring the possibilities of AI and algorithms.

JournalInteractive Learning Environments
Journal citation29 (4), pp. 523-526
ISSN1049-4820
1744-5191
Year2021
PublisherTaylor & Francis
Digital Object Identifier (DOI)https://doi.org/10.1080/10494820.2021.1940485
Web address (URL)https://doi.org/10.1080/10494820.2021.1940485
Publication dates
Published19 May 2021

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