Artificial Intelligence for Decolonising

Eric Atwell (School of Computing)

Project overview

The university is investing heavily in decolonising, through its key principles.

I will apply artificial intelligence and learning analytics to our teaching and learning data, to identify and measure colonial bias in our curriculum. This includes automated review of course reading lists in different disciplines to measure their colonial bias and diversity. It is widely assumed that decolonizing only applies to a few disciplines; I will seek to analyse teaching and learning data from a range of schools and faculties, to show whether “decolonising” applies beyond humanities and social sciences.

Key findings

• No university-wide data is available for analysis of decolonization of reading lists.
• University Library reading lists system provides no evidence of decolonizing reading lists.
• Quality assurance module reviews provide no evidence of decolonizing reading lists.
• Approximately 0% of taught module reading lists have been decolonised.

Implications for practice

• The Library should keep data on changes to Reading Lists over time, and publish this
data-set for future research as an open access research data-set, at Research Data Leeds
Repository (University of Leeds, 2024e).
• Quality Assurance office should collect and publish data on all taught modules that have
been updated to decolonize the reading list and/or curriculum; this research data should
be an open access research data-set at Research Data Leeds Repository (University of
Leeds, 2024e).
• Artificial Intelligence and Natural Language Processing techniques should be applied to
analyse various types of bias within university text: Leeds University news articles,
reading lists, lecture notes and transcripts, textbooks, e.g. (Bin Shiha et al, 2023b). This
aligns with broader EDI strategic goals of the university.

If you want to find out more details about this fellowship or what the next steps were upon completion please read the full snapshot or contact Eric (E.S.Atwell@leeds.ac.uk)

Project start date: 1 September 2022