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CSC-10075 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

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

CSC-10075 - Data Ethics, Governance and Security
Coordinator: Baidaa Al-Bander
Lecture Time: See Timetable...
Level: Level 4
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

The rise of AI and Data Science and AI within society has been filled with potential but has also come with increasing risk and harms. This includes risks to personal privacy and freedoms, impact of discriminatory algorithms due to algorithmic bias and online harms. As a result we see a mixture of over-confidence in the value and accuracy of AI systems resulting in tech solutionism in juxtaposition to increasing criticism and lack of public trust. In response to this the need for ethically aligned design of AI and data science projects has grown. This module will outline the risks and harms associated with data science and AI by utilising a case study approach and will explore the body of regulation, governance frameworks and techniques. This will provide the students the opportunity to develop the skills and knowledge required by employers for ethically aware data scientists.
It will help the students evaluate the digital services provided by service providers for accurate requirements gathering and professionalism. Furthermore, It will help develop appropriate communication skills, study skills, and report writing.

Aims
This module will provide the students with an understanding of Data Ethics, Governance and Professionalism. They will explore relevant regulations, governance frameworks and standards required for ethically aligned design and how these relate to different aspects of the data science lifecycle. The students will apply this knowledge via techniques and tools that can be applied to areas such as data anonymisation, debiasing, and fairness testing. It will also enable students to understand the basis and practice of professional software and systems engineering as applicable to data science; to understand the fundamentals of requirements, evaluation, and professionalism; and to develop appropriate communication and study skills.

Intended Learning Outcomes

Evaluate the role of professional and ethical responsibilities in the development and deployment of data-driven technologies: 1
Communicate ethical risks, governance concerns, and professional obligations clearly to both technical and non-technical audiences: 1
Explain key ethical principles in data science and AI, including fairness, accountability, transparency, and respect for privacy: 2
Identify and apply relevant legal and regulatory frameworks (e.g. GDPR, Data Protection Act) to data collection, processing, and sharing: 1,2
Recognise common sources of bias in datasets and algorithms, and describe basic techniques for identifying and reducing bias: 2
Articulate data governance principles, including data quality, anonymisation, and secure data handling practices: 2

Study hours

28 Online Lectures
36 Active Practical Learning
200 Private Study
36 Completing Coursework

School Rules

None

Description of Module Assessment

1: Group Assessment weighted 40%
Group Project and Presentation
Group Project and Presentation: The first assessment component is a group project report and presentation focused on applying requirements and evaluation techniques to a relevant real-world case study. This part of assessment aims to test the students' ability to collaboratively analyse, design, and evaluate data-driven solutions while considering ethical and governance factors. The groups will be formed between 4-5 students to make sure diversity of thought. The groups must communicate the results via a written report of 1,000 words excluding appendices, and a group oral presentation lasting 20 minutes per group.

2: Assignment weighted 60%
Ethics, Governance and Security
Individual Case Study: The second assessment component is an individual assignment case study, which will involve both practical and theoretical elements related to ethics and governance in data science. This assignment will draw on the skills and knowledge acquired throughout the module, challenging students to critically analyse ethical dilemmas and governance frameworks in real-world scenarios. The word limit for this assignment is 2000 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