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

Programme/Approved Electives for 2024/25

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2024/25

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.

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. 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

Apply the different methods for data anonymisation and psuedoanonymisation and how to protect identification.
: 2
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.: 2
Compare the data security techniques that can be employed to ensure data privacy and security.: 2
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.: 2
Evaluate communication skills appropriate to professional software, systems engineering, and data science.: 1
Apply techniques associated with establishing the requirements and evaluation of requirements by seeking feedback from stakeholders.: 1
Discuss the tenets of professional, legal and ethical practice involved in the sustainable exploitation of data science and computer technology.
: 1

Study hours

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

School Rules

None

Description of Module Assessment