CSC-44104 - Ethics, Governance, and Professionalism in AI and Data Science
Coordinator: Sandra Woolley Room: N/A Tel: +44 1782 7 33259
Lecture Time:
Level: Level 7
Credits: 30
Study Hours: 300
School Office: 01782 733075

Programme/Approved Electives for 2026/27

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2026/27

Embark on a transformative journey into key professional standards that underpin current and future AI and Data Science. You will delve into critical topics like fairness and bias, privacy and ethical frameworks; and explore the impact of regulations and compliance and AI’s impact on society. To support you in preparing for future employment, you'll access cutting-edge research in areas like explainable and responsible AI, gain insights from invited industry speakers, and receive expert guidance from our dedicated employability and careers team.

Aims
This module aims to enable students to:
• understand ethics, governance, and professionalism relevant to AI and data science
• learn about the complex nature of bias, privacy, and fairness through real-world examples
• analyse datasets, apply methods, learn from practitioners, and articulate key challenges

Intended Learning Outcomes

Articulate professional codes of conduct, ethical concepts and key aspects of laws and regulations relevant to AI and data science: 1,2
Recognise the complexity of bias, privacy and fairness issues in real world data: 1,2
Analyse datasets and apply methods relevant to privacy, bias and fairness: 2
Contribute individually or in groups, to constructive and informed debates on data ethics: 1
Demonstrate the knowledge, skills and behaviours of a professional data scientist and identify required future learning: 1

Study hours

90 hours active learning
• 42 hours lectures (7 hours of lectures each week will include approximately 2 hour of facilitated class discussion on a range of relevant topics). This will also include up to 3 x 1 hour guest lectures will be included from expert practitioners and researchers.
• 24 hours practical/tutorial/workshop (4 hours of guided practical/tutorial/workshop activities will be scheduled each week)
• 24 hours group work (students will meet in their groups for 4 hours each week to progress their group work)
210 hours independent study
• Background Reading & Preparation (100 hours): Reviewing core textbooks, academic papers, and lecture pre-readings before classes.
Post-Lecture Review & Consolidation (55 hours): Re-watching lecture recordings, organizing notes, clarifying confusing concepts, and creating summary sheets.
Assessment Preparation & Research (55 hours): Conducting literature searches, outlining, drafting, and refining coursework or essays.

School Rules

None

Description of Module Assessment

1: Group Assessment weighted 60%
Group Research Paper (IEEE Format Using LaTeX)
This assessment requires students to investigate a contemporary topic relevant to the module and demonstrate their ability to conduct independent research, critically evaluate academic literature, and communicate findings effectively. The assessment comprises three components: a written research paper (40%), a poster (20%) and an oral presentation (50%). In group of 4-5 students will undertake a research project and produce a 4-page, two-column research paper (excluding references) written in IEEE conference format using LaTeX and produce a poster for presentation. The paper should demonstrate the ability to critically analyse domain-specific challenges related to topics such as Artificial Intelligence, data ethics, professionalism, and responsible innovation in contexts including healthcare, finance, welfare systems, education, and academic research. The submission requires three components: a written research paper (40%), a poster (20%) and group presentation of the paper (40%). The presentation should communicate the research aims, key findings, critical analysis, and conclusions in a clear and professional manner. All group members are required to contribute to and deliver a portion of the presentation and must demonstrate an understanding of the group's research. The final submission must include: 1. A PDF version of the research paper in IEEE conference format. 2. The complete LaTeX source files (overleaf link is also acceptable). 3. A poster 4. Delivery of the presentation during the scheduled assessment period. This assessment contributes 60% of the overall module mark.

2: Reflective Diary weighted 40%
A reflective diary summarising practical work and reflecting on module components based on real word issues and practice
Students will be set weekly tasks to complete (practical activities, reading and synthesizing tasks, reflections on in class discussions and guest speakers) that relate to the content delivered that week. A digital template will be made available where students evidence and reflect on these tasks. In each reflection, the students will use a reflective learning model to support them in reflecting on their learning experience, describing what they have learnt, identifying their strengths and identifying next steps for their learning. The final submission will also include an overall reflection on what this means for their current and future career development. This portfolio is equivalent to 2,500 words.