TOPICS IN PEOPLE-CENTRED AI - 2023/4
Module code: MANM519
Module Overview
This module offers an introduction to the use of AI in society, work, media and communication, government and policy. It puts people ¿ as opposed to technology -- in the centre of AI, and highlights core considerations in planning for AI applications as a response to issues and considerations in the society. This is done by exploring varied positions of users and stakeholders in relation to AI, examining the suitability of AI and associated tools and methods to the productivity/progress/propagation in society. In this module we discuss the opportunities, but also the challenges, risks, threats and ethical implications involved in the use of AI in society.
Module provider
Surrey Business School
Module Leader
HERAVI Bahareh (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 100
Lecture Hours: 22
Seminar Hours: 11
Guided Learning: 11
Captured Content: 6
Module Availability
Semester 1
Prerequisites / Co-requisites
None
Module content
Indicative content includes:
¿ AI in society - citizenship, access, communication, transparency, inclusivity and progress.
¿ AI bias and algorithmic accountability.
¿ AI at work - AI in human resource management, collaboration between human employees and AI systems, future AI workplace and workforce.
¿ AI and policy - principles to guide AI powered decision making, enforcement, compliance and information. Planning, resourcing and barriers for practical implementation.
¿ AI and authority - preserving fundamental freedoms- privacy, building public trust, public priorities, power and stakeholder views.
¿ AI and media - Data & Computational Journalism, fact-checking, data-driven storytelling.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Oral exam or presentation | Group Demonstration | 40 |
| Coursework | Group Project/Essay | 60 |
Alternative Assessment
In exceptional cases, where a student is unable to work in a group due to extenuating circumstances, they will attempt the same essay for the 'Group Project/Essay' assignment individually. No alternative assessment is available for the 'Group Demonstration' assignment.
Assessment Strategy
The assessment strategy for this module is designed to provide students with the opportunity to demonstrate subject-specific knowledge and a critical understanding of the use of AI in society in line with the learning outcomes. The summative assessment for this module consists of a group critical project/essay, and a group demonstration. The topics and details will be provided during the term. Formative assessment is provided via feedback during scheduled class/seminar sessions in the following ways: ¿ During lectures, by question and answer sessions ¿ During group activities
Module aims
- The aim of this module is to equip students with the knowledge, skills and ability to critically understand and address issues in the use of AI in society. The use of AI will be explored in both a restorative and progressive context.
Learning outcomes
| Attributes Developed | ||
| 001 | Demonstrate an understanding of the role of AI in society. | CT |
| 002 | Describe and critically evaluate opportunities and risks that data and computational tools and methods bring to society, work, media, and policymaking | CKPT |
| 003 | Recognise and articulate ethical implications in the use of AI in society | CKPT |
| 004 | Be able to conceptually codify elements of a societal issue for the purpose of matching AI powered resolutions | KPT |
| 005 | Be able to plan for the resourcing, implementation and governance of AI in society | CKPT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy promotes critical thinking, problem-solving and active engagement. The mode of delivery is blended and will include weekly lectures and seminars, along with screenings, captured material, active and task-based learning, group activities, as well as independednt and guided learning.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM519
Other information
This module explores dynamic and fast changing topics. As such, the reading list is dynamic and the most appropriate and up-to-date readings will be provided during the semester.
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.