Module manager: Yen Nee Wong
Email: y.wong@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
This module is not approved as an Elective
The module examines how AI technologies shape what is known, who is heard, and who benefits from AI usage. It explores how power circulates through platforms, datasets, infrastructures, and labour. The module draws on perspectives and concepts such as epistemic justice, decolonial thought, feminist science and technology studies, queer theory, disability justice, and critical race perspectives, to unpack how AI systems are built and whose knowledge is extracted or excluded in the process. Alongside conceptual work, the module cultivates practical capacities: diagnosing harms across the AI lifecycle; mapping actors and accountability; and designing remedies and redress. A recurring focus is on everyday practices of resistance and change, such as ethical refusal and consent, accessible design demands, participatory auditing, data governance challenges, equitable procurement, and community‑led alternatives.
The module aims to develop a justice-centred understanding of AI. This involves analysing how structural arrangements produce bias, exclusion and the dominance of certain viewpoints while constraining individual and community agency. To achieve these aims, the module blends framing lectures with workshop discussions and case-based activities that connect to industry and lived experience. Learning activities will be used to cultivate everyday practices of resistance and change, so students can develop practical agency to exercise informed agency in their own use of AI‑driven platforms and to contribute to institutional change. Co-teaching across departments and with industry partners, together with problem-led group projects, will create opportunities for students to develop skills in interdisciplinary collaboration and ensure they can translate critical insights into concrete interventions in both policy and practice.
On successful completion of the module students will be able to:
1: Diagnose and explain bias across the AI lifecycle and analyse the structural drivers that shape sector-specific ethical implications;
2: Apply critical frameworks to analyse concrete AI cases and articulate the ethical and social justice implications;
3: Evaluate data governance practices and propose context-appropriate improvements and safeguards;
4: Synthesise different sources to construct critical analysis of digital systems. (Academic, Work Ready, Enterprise and Sustainability skill);
5: Use policy, process and product interventions to justify a feasible, risk aware implementation roadmap for digital systems. (Academic, Work Ready, Enterprise and Sustainability skill);
6: To deploy diversity awareness in delivering and communicating project work. (Work Ready, Enterprise and Sustainability skill).
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Seminar | 2 | 1 | 2 |
| Seminar | 9 | 2 | 18 |
| Private study hours | 130 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 150 | ||
130
Formative feedback is provided through a blend of timely, actionable and accessible formats. Students receive immediate verbal feedback in workshop sessions on low-stake case-based activities. Mid-module, each project group submits a 500-word project plan, in which they will receive annotated comments from the module leader. Students will also be encouraged to consult with module staff during open door sessions for formative feedback. Structured peer review will also be encouraged during workshops to support group work. At the request of students, formative feedback can also be delivered in alternative formats to meet accessibility needs.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework 1 | 100 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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