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IS40970

Academic Year 2024/2025

AI & Ethics (IS40970)

Subject:
Information Studies
College:
Social Sciences & Law
School:
Information & Comms Studies
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Susan Leavy
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Artificial Intelligence systems are becoming increasingly influential in society. The purpose of this module is to equip students with a comprehensive understanding of AI without the requirement of a technical background. The goal of the module is that students will be able to critically engage with the development and evaluation of AI systems from a multi-disciplinary perspective. The history of AI is examined along with its philosophical foundations and implications in terms of society and ethics. The course presents an overview of different branches of AI and contemporary debates in relation to approaches to AI. From this module students will gain a solid understanding of the foundations of AI, how it has evolved over time and current trends and debates in AI development.

About this Module

Learning Outcomes:

On successful completion of this module students should be able to:

1. Demonstrate an overall understanding of AI and its approaches
2. Critically evaluate different approaches to the development of AI and their implications from a societal perspective.
3. Use an AI technology in a research project
4. Engage in contemporary debates in the field of AI
5. Critically evaluate and assess the development of AI systems

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

101
Lectures

24
Total

125

Approaches to Teaching and Learning:
This module includes lectures, reflective learning, critical reading, class activities, debates and project work.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Written report on AI topic Week 4 Graded No
25
 No
Assignment(Including Essay): Report on AI Topic Week 8 Graded No
25
 No
Assignment(Including Essay): Report on AI project Week 14 Graded No
50
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Feedback will be provided to students on Brightspace.

Name Role
Dr Elizabeth Farries Lecturer / Co-Lecturer
Dr Thompson Kwarkye Lecturer / Co-Lecturer
Megan Nyhan Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33 Fri 13:00 - 14:50
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