Keele Badge

CSC-44104 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2025/26 Last Updated: 04 December 2025

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

Programme/Approved Electives for 2025/26

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2025/26

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
Recognise the complexity of bias, privacy and fairness issues in real world data: 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: 2

Study hours

96 hours active learning
• 48 hours lectures (8 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)
204 hours independent study
• Students will be provided with guidance for their independent learning aimed at scaffolding their understandings.

School Rules

None

Description of Module Assessment

1: Group Assessment weighted 60%
A formal technical report and an accompanying poster summarising key challenges
Groups of up to five students will produce a technical report and accompanying posters demonstrating their ability to articulate relevant domain challenges such as AI, data ethics and professionalism in healthcare or finance, or in the welfare system or academic research. Students will be guided on effective group working and will be provided with a dedicated Teams channel to support their work. They will meet for 2-hrs each week to progress their work. They will be provided with an outline of the report and poster contents and detailed instructions on the presentation and referencing requirements. Students will be required to complete a peer review form so that individual contribution levels can be determined and whether adjustments to the group mark are needed. The technical report and posters will be equivalent to approximately 6,500 words excluding references and appendices.

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, online exercises, 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.

The University undertakes a continuous review of its teaching and research provision to ensure programmes are of a high quality and changes may be made to modules where it is necessary and reasonable to do so. Please be aware that minor changes to modules, such as a change to an individual assessment, can occur up until enrolment, although we endeavour to give you as much notice as possible of any changes. Details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/legalgovernancecompliance/governance/actcharterstatutesordinancesandregulations/studenttermsconditions/

Last Updated: 04 December 2025