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SOC41130

Academic Year 2024/2025

AI and Society (SOC41130)

Subject:
Sociology
College:
Social Sciences & Law
School:
Sociology
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr David Anzola
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Generative artificial intelligence is spearheading a major digital revolution that, some argue, has the potential to completely reshape human experience. This module explores the technological, social, political, and economic drivers and implications of the progressive popularisation of AI. It provides students with a broad understanding of key technical aspects of the generative AI revolution (for example, its relationship with other digital technologies and the technological infrastructure supporting it) and their interplay with various social dynamics and institutions (including, most prominently, issues with data, technological asymmetries and inequalities, the politics of technology, intellectual property and regulation, and monetisation). Through the module, students will critically analyse and reflect on a variety of current and future scenarios, such as e-health, autonomous driving, or the future of work, to clarify the contextual nuances of the application of AI technologies and their potential, as a whole, to deliver on the promise to completely change the way we live.

About this Module

Learning Outcomes:

On completion of this module, students will be able to:
• Identify technological developments supporting the current generative AI revolution.
• Explain the social dimension of technical and technological change.
• Analyse different contexts of application of AI technologies.
• Critically assess the limits of current AI.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

80
Autonomous Student Learning

180
Lectures

24
Total

284

Approaches to Teaching and Learning:
Active/task-based learning
Peer and group work
Lectures
Critical writing
Reflective learning

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 text critically analysing the input-output connection in an interaction with a chatbot. Week 12 Standard conversion grade scale 40% No
60
 No
Participation in Learning Activities: Continuous assessment including in- and out-of-class activities centred on the critical exploration of the interplay between AI and society.
Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11 Standard conversion grade scale 40% No
40
 No

Carry forward of passed components
No
 

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

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

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, 32, 33 Tues 11:00 - 12:50
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