CSC-30053 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

Programme/Approved Electives for 2022/23

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2022/23

This module will provide the students with an understanding of Data Ethics and Security. 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. They will apply this knowledge via techniques and tools that can be applied to areas such as data anonymisation, debiasing and fairness testing.

Intended Learning Outcomes

Debate general ethical concepts, such as deontological and teleological ethics, and consider how they can be applied to data science and evaluate how AI relates to aspects of Human Rights and UN Sustainable Development Goals
: 1
Apply the different methods for data anonymisation and psuedoanonymisation and how to protect identification.
: 1
Analyse datasets and algorithmic outcomes for bias by using summative statistics, performance metrics and fairness tests. This will include demonstrating an understanding of protected characteristics and proxy features and methods for debiasing and/or mitigating for bias: 1
Compare the data security techniques that can be employed to ensure data privacy and security: 1
Test the legislative and governance landscape associated with ethical data science including key legislation such as GDPR, Data Protection Act and international standards and ethics panels: 1
Debate the role of Explainable AI (XAI) methods and techniques in their provision of Data Science and AI solution that can be understood by humans
: 1

Study hours

12 hours practical work
6 hours of tutorials
14 hours of online lectures
118 independent learning

School Rules

None

Description of Module Assessment