2021/22 Taught Postgraduate Module Catalogue
OCOM5202M Ethics of Artificial Intelligence
15 creditsClass Size: 100
Module manager: Dr Andrew Kirton
Taught: 1 Jul to 31 Aug View Timetable
Year running 2021/22
This module is not approved as an Elective
Module summaryThe 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.
ObjectivesThe 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.
Students will be able to identify and comprehend ethical issues in a professional context
Students will be familiar with several key areas of ethical concern in the intersection of technology and the workplace
Students will be able to analyse applied ethical arguments to masters level
Students will be able to make and defend applied ethical arguments to masters level
Students will be able to communicate applied ethical analysis and argument through written and verbal presentation to masters level
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
|Delivery type||Number||Length hours||Student hours|
|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 studyPrivate 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 FeedbackOnline 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
|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 listThe reading list is available from the Library website
Last updated: 11/04/2022
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