Module manager: Linhao Fang
Email: l.fang1@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
This module is not approved as an Elective
Artificial intelligence (AI), and in particular machine learning, is increasingly used in organisations and society to inform decisions and to deliver products and services. AI provides tremendous capabilities, enabling individuals and organisations to achieve transformational objectives. However, contemporary AI presents a range of significant challenges. For example, contemporary AI lacks transparency in how it derives its predictions or recommendations (the ‘black box effect’), and its outputs are often ambiguous. Due to its data-driven nature, AI tends to reproduce and reinforce existing biases and inequalities in organisations or society. This presents serious challenges and risks for individuals, organisations and society. Efforts are being made worldwide to identify methods and approaches to design, build, deploy and use AI tools in a responsible and accountable way, and capable of addressing people’s real needs. When is AI the right tool — and when is it disruptive or harmful? This module equips students with the knowledge and skills to critically assess the developmental lifecycle of AI systems and manage its consequences in organisations and society. Students will explore AI use cases in different domains and sectors (e.g., the workplace, healthcare, education, militarisation, smart cities) to critically analyse AI’s virtues, failures and limitations, and to consider how society can make AI more human-centred.
The focus of this module is on developing a critical understanding of AI as an emerging and evolving technology that is becoming pervasive across all walks of life. AI systems are increasingly deployed, but their related ethical issues and risks are often overlooked. Through this module, students will develop a critical understanding of the opportunities and challenges associated with designing, developing, implementing, using, and evaluating AI systems.
The module examines current ideas, theories, and approaches proposed to build and evaluate AI, and to inform decisions about its development and deployment, with a view to delivering AI systems that are responsive to human (individual and societal) needs.
Students will develop their understanding and skills through a series of seminars and guest speaker sessions, with a strong emphasis on in-class discussion and interactive activities.
The module includes scenario-based AI case analysis workshops (e.g., autonomous transport systems in urban environments, decision support in university admissions), where students apply concepts to realistic deployment dilemmas. These workshops involve role-based discussion and structured debate to explore stakeholder perspectives, ethical trade-offs, and governance challenges.
Additional learning opportunities are provided through guided AI-focused coursework, where students exercise critical thinking through engaging with ongoing debates around AI deployment, emphasising analysis, evaluation, and reflection.
On successful completion of the module students will be able to:
1. Demonstrate critical literacy of theories and approaches informing the responsible development of AI technologies;
2. Critically evaluate AI applications, assessing their capabilities, implications, and consequences.
3. Produce well-justified and insightful strategies for governing and managing AI in organisational and societal contexts, to promote more human-centred outcomes;
4. Critically evaluate claims about technology capabilities, risks, and impacts using appropriate evidence and reasoning. (Academic, Sustainability and Work Ready skill);
5. Locate, evaluate and synthesise information from theoretical, policy and case-based source. (Academic, Digital and Work Ready skill);
6. Reflect critically on personal assumptions, values and professional responsibilities in relation to technology and the future of work (Academic, Enterprise and Work Ready skill);
7. Develop and communicate structured, evidence-based arguments. (Academic and Work Ready skill).
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Seminar | 4 | 2 | 8 |
| Seminar | 11 | 2 | 22 |
| Private study hours | 270 | ||
| Total Contact hours | 30 | ||
| Total hours (100hr per 10 credits) | 300 | ||
270
Students will be provided with assignment-specific exercises with live feedback during certain sessions. A discussion board will be set up to facilitate feedback on questions regarding the assignment.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 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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