Module manager: David Wong
Email: d.c.wong@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
As per programme entry requirements
This module is not approved as an Elective
Data science and Artificial Intelligence (AI) research often requires data recorded from individuals. Such data needs to be handled in accordance with the law and ethical principles. This module introduces students to the legal, ethical and governance frameworks that are applicable to health data science and AI. Students will develop their ability to analyse health data science projects with respect to their legal, ethical and governance implications.
The module objectives are:
- Provide students a solid grounding in the legal, ethical and professional guidelines that are relevant to research data use including Data Protection law, confidentiality and privacy, the principles of medical/research ethics and relevant GMC/NMC codes of practice
- Enable students to recognise and deal appropriately with legal and ethical dilemmas in research data use, including the use of artificial intelligence in research.
- Equip students with the ability to evaluate the applicability and usefulness of a range of 'privacy enhancing techniques'
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
LO1 Describe and critically evaluate key legal, ethical and governance frameworks that are applicable to health data science in the United Kingdom
LO2 Critically analyse health data science projects with respect to their legal, ethical and governance implications
LO3 Demonstrate a critical understanding of the technical and organisational safeguards that can be applied in health data science projects
LO4 Demonstrate a sophisticated understanding of key legal, ethical and governance challenges posed by artificial intelligence, and how these differ from traditional health data science.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1- Critically assess how lack of diversity can be an ethical problem, particularly for AI methods in data science
2- Apply ethical frameworks to data science scenarios, assess the ethical consequences of proposed projects, demonstrate an understanding of the ethical and legal frameworks involved in data science, and apply them to real-life scenarios
3- Critically evaluate ethical and legal frameworks in the context of how they are applied to data science research
- Legal frameworks applicable to health data science within the United Kingdom, including frameworks relating to privacy, data protection and confidentiality
- Ethical frameworks applicable to health data science, including frameworks relating to medical research and data-driven technologies
- Ethical approval for research in the UK- Legal and Ethical implications of AI in health data research
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 13 | 2 | 26 |
| Practical | 2 | 2 | 4 |
| Private study hours | 120 | ||
| Total Contact hours | 30 | ||
| Total hours (100hr per 10 credits) | 150 | ||
The teaching format will encourage students to discuss and seek clarification from staff during the sessions.
Students will undertake a group learning task on a selected topic which will require the development and delivery of a presentation to peers at the end of the module.
Group presentations will be prepared and delivered by students as part of the module to provide an opportunity for ongoing feedback from teaching staff and peers. In small groups (3-4 per group), students will prepare and deliver an analysis of the law, ethics, and governance issues surrounding a scenario involving the use of healthcare data for research. The group is responsible for developing the scenario. Verbal feedback will be provided immediately following the presentation. Students will peer-review the presentations of other groups and share comments. Teaching staff will provide feedback comments to each group.
Students will submit a formative 500 word answer to an example question in a similar style to the summative work, for which we provide detailed written feedback. Students will analyse one example case study, in the same format as the summative report.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Formative - Group presentation | 0 |
| Coursework | Formative - 500 word practices question – review and analysis of 1 health data science scenario | 0 |
| Coursework | Summative - Summative report - review and analysis of multiple health data science scenarios (3000 words) | 100 |
| Total percentage (Assessment Coursework) | 100 | |
The summative report will require students to analyse multiple provided case studies / scenarios with respect to applicable legal and ethical frameworks and to determine appropriate decisions / courses of action with respect to the scenario. Students who fail the first attempt at the summative coursework will be offered an opportunity to resubmit. The resubmission will take the same format as the first attempt.
There is no reading list for this module
Last updated: 08/05/2025
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