2024/25 Taught Postgraduate Module Catalogue

OCOM5202M Ethics of Artificial Intelligence

15 creditsClass Size: 100

Module manager: Dr Andrew Kirton
Email: a.kirton@leeds.ac.uk

Taught: 1 Jul to 31 Aug View Timetable

Year running 2024/25

This module is not approved as an Elective

Module summary

The workplace (and home) is (are) going through a technological revolution. New automated algorithms can make medical assessments or deliver financial advice, for example, faster and more accurately than human professionals. Customers are given financial advice by chatbots and other AI systems. Medical data is collected by fitbits and AI can deliver medical judgements, meaning that they have no need to interact with another human at all. And more information about customers and potential customers is collected, collated and analysed every day, allowing for targeted marketing and accurate risk analysis. Ostensibly, these technologies are being introduced in order to make commerce, business and medicine more efficient, more accurate, and more objective. Nonetheless, with new technologies come new problems, and the rapidity with which these technologies are being applied often mean these problems go unnoticed until serious ethical and legal issues emerge. This module provides students with the analytical and theoretical tools to engage with these issues in a professional context. The module is taught online alongside modules in maths and computing in which these issues are live. There are eight online units. The online units will comprise of online learning materials such as videos and online documents, some related reading, and a facilitated online discussion forum per topic. There will be group work and group presentations which focus on discussion of concrete case studies in AI.

Objectives

The workplace (and home) is (are) going through a technological revolution. In particular, the combination of automation and machine learning may take many consequential decisions that are currently made by human beings and delegate them to computer algorithms. This module provides students with the analytical and theoretical tools to engage with the ethical questions that this raises, such as: Who is morally responsible when an automated system makes a mistake? Who is morally responsible if a self-learning algorithm develops and enacts prejudices? Is it a good idea to remove the need for interpersonal trust from professional interactions? What rights do consumers have over their personal information? How do organisations respect clients’ best interests, and the public interest, in gathering, storing, and using data? Students will apply the academic skills learned in the module to concrete issues and case studies in AI.

Learning outcomes
1. Students will be able to identify and comprehend ethical issues in a professional context
2. Students will be familiar with several key areas of ethical concern in the intersection of technology and the workplace
3. Students will be able to analyse applied ethical arguments to masters level
4. Students will be able to make and defend applied ethical arguments to masters level
5. Students will be able to communicate applied ethical analysis and argument through written and verbal presentation to masters level

Syllabus

The module will cover a range of the key ethical issues relevant to the introduction of AI and big data, including, for example:

- Introduction to machine ethics
- Big data: consent and privacy
- Big data: surveillance and responsibility
- AI and automation: machine learning and bias
- AI and automation: professionalism and responsibility
- AI and automation: assessing vulnerability, best interests and the public interest
- The ethics of interpersonal and social trust

Teaching methods

Delivery type	Number	Length hours	Student hours
On-line Learning	6	1.00	6.00
Group learning	6	2.00	12.00
Independent online learning hours			28.00
Private study hours			104.00
Total Contact hours			18.00
Total hours (100hr per 10 credits)			150.00

Private study

Private study will include directed reading and self-directed research in support of learning activities and discussions, as well as in preparation for assessments. 

Independent online learning involves non-facilitated directed learning. Students will work through bespoke interactive learning resources and reflective activities in the VLE.

Opportunities for Formative Feedback

Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow self and peer assessment providing opportunities for formative feedback from peers and tutors. 

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
In-course Assessment	Group discussion forum	20.00
In-course Assessment	Video presentation on case study	80.00
Total percentage (Assessment Coursework)		100.00

This module will be reassessed by a single piece of coursework.

Reading list

The reading list is available from the Library website

Last updated: 29/04/2024 16:18:45

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