Module manager: Dr Paula Satne
Email: P.Satne@leeds.ac.uk
Taught: 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec (2mth)(adv yr) View Timetable
Year running 2025/26
N/A
This module is not approved as an Elective
This module provides students with the analytical and conceptual tools to identify and evaluate the ethical, legal, and societal implications of AI systems. Students identify and comprehend key ethical issues and areas of concern in the development and deployment of AI systems, including issues related to fairness, bias, transparency, privacy, surveillance, and accountability. Students analyse real-world case studies and are encouraged to develop ethical arguments in response to complex dilemmas. The module fosters responsible AI practice across technical and interdisciplinary contexts.
This module aims to develop students’ ability to identify and evaluate the ethical, legal, and societal implications of artificial intelligence. Through a combination of ethical reasoning, case-based analysis, and applied discussion, students will explore concepts such as fairness, bias, transparency, and accountability. The module also aims to help students think intelligently and confidently about the ethical dimensions of AI and to view subsequent technical modules through a normative lens, fostering a reflective and responsible approach to AI practice. Learning activities are designed to encourage critical reflection, ethical reasoning, and interdisciplinary awareness, enabling students to critically evaluate real-world AI applications.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Identify and comprehend ethical issues and areas of concern in the development and deployment of AI systems.
2. Identify and examine the societal and environmental impacts of AI technologies, including issues arising from data collection and management.
3. Examine several pertinent real-world case studies, developing ethical arguments to postgraduate level.
4. Reflect on the relationship between ethics, the law, and regulatory frameworks.
5. Make, defend, and communicate ethical arguments to postgraduate level in relation to practical applications of AI technologies in society.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Apply logical and analytical reasoning to identify and analyse key ethical issues, such as bias, fairness, and transparency in AI systems.
2. Apply ethical reasoning to real-world case studies.
3. Identify ethical issues in AI development and application.
4. Exercise critical evaluation to evaluate social and environmental impacts of AI deployment in public and private sectors.
5. Communicate complex ethical analysis and arguments clearly and effectively.
6. Collaborate effectively to identify responsible AI practices in diverse contexts.
Indicative content for this module includes:
1. Core ethical frameworks and methods for analysing AI technologies and their societal impact
2. Big data ethics: consent, privacy, surveillance, and responsibility in large-scale data use
3. Bias, fairness, and accountability in machine learning and automated decision-making
4. Environmental and societal implications of AI development, deployment, and automation
5. Assessing vulnerability, best interests, and the public interest in the design and use of AI systems
6. Value alignment, human agency, and questions of consciousness in the context of artificial general intelligence.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Discussion forum | 6 | 1 | 6 |
| WEBINAR | 6 | 1 | 6 |
| Independent online learning hours | 42 | ||
| Private study hours | 96 | ||
| Total Contact hours | 12 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow self and peer assessment providing opportunities for formative feedback from peers and tutors.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Group Discussion | Group Discussion Forum | 20 |
| Assignment | 15 minute video presentation | 80 |
| Total percentage (Assessment Coursework) | 100 | |
This module will be reassessed through a 100% individual assessment in the same format as Assessment 2 (video presentation). The reassessment which will involve a presentation that requires students to apply and integrate the knowledge and skills developed across all learning outcomes.
Check the module area in Minerva for your reading list
Last updated: 18/02/2026
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