Security, Artificial Intelligence and Emerging Technologies

Module title	Security, Artificial Intelligence and Emerging Technologies
Module code	SPAM002
Academic year	2025/6
Credits	30
Module staff	Dr Lewys Brace (Lecturer)

Duration: Term	1	2	3
Duration: Weeks	11	11

Number students taking module (anticipated)	30

Module description

Advancements in emerging technologies, including biotechnology, quantum computing, 3D printing, and particularly artificial intelligence (AI), pose new security concerns, such as aggravating social tensions and norms, creating new security vulnerabilities, and encouraging relative power shifts at both the inter-nation state and state versus sub-state actor levels. At the same time, these technologies have been employed in areas such as homeland security and crime prevention to protect individuals, assets, and sensitive data. This module will introduce you to AI and other emerging technologies, their impact on the security landscape, and provide you with the skills necessary to implement small-scale AI algorithms.

Module aims - intentions of the module

This module has two main aims. The first is introduce you to how emerging technologies, especially artificial intelligence (AI), are changing the security domain. It will look at how these technologies have been utilised not only by state actors, such as militaries, homeland security, and law enforcement, but also how smaller/unfriendly states, non-state actors, and other groups are adapting to these technologies and utilising them for their own gain. This requires looking at security in a broad sense, and will involve you learning about such things as the use of AI by police forces to detect and prevent crime, how AI is impacting the spread of disinformation and having a destabilising impact on societies, the use of generative AI by extremist actors, the use of AI in intelligence gathering, emerging technologies and military applications, and more. The second aim involves you developing the skills necessary to implement a basic AI algorithm for a security application.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

1. Demonstrate a strong knowledge of how the security, defence, and law enforcement domains are changing as a result of emerging technologies.
2. Demonstrate the ability to implement an algorithm for a potential security application.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

3. Critically reflect on the role and impact of data, algorithms, and computational power within the wider context of society more generally.
4. Critically reflect on the content of the module within the broader context of the digitalisation of society.

ILO: Personal and key skills

On successfully completing the module you will be able to...

5. Demonstrate written analytical skills by producing an essay and technical report to a deadline.
6. Demonstrate the ability to discuss technical details, i.e. how algorithms work, in concise and informative manner.

Syllabus plan

Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following themes:

What is artificial intelligence (AI)?
Data, algorithms, and computational power
Adversarial AI.
Emerging technologies.
Ethics of AI and other emerging technologies.
Emerging technologies and the state.
Emerging technologies, inter-state rivalries, and the international system.
Artificial intelligence and autonomous weapons systems.
Data and algorithms and intelligence gathering.
Data, algorithms, AI, and dis/misinformation.
Online extremism and technology.
Synthetic media.
Data, algorithms, and policing.
Emerging technologies and domestic/homeland security issues in wider society.
Cyberwar and cyberweapons.
Emerging technologies and space conflict.
Building resilience.
AI governance and law.
The wider impact of technological developments in several important socio-political contexts; i.e. water and food security, climate change, migration.
The future of the relationship between emerging technologies, society, and security.

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
22	278	0

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Learning and Teaching Activities	22	22 x one-hour lectures
Guided Independent Study	64	Course readings
Guided Independent Study	64	Reading/research for essay
Guided Independent Study	150	Research for technical report

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Oral discussion with module convener in preparation for technical report submission	10 minutes	2, 6	Oral feedback

Summative assessment (% of credit)

Coursework	Written exams	Practical exams
100	0	0

Details of summative assessment

Form of assessment	% of credit	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Essay	25	2,500	1, 3, 4, 5	Written feedback
Essay	25	2,500	1, 3, 4, 5	Written feedback
Technical report	50	2,500	1, 2, 5, 6	Written feedback
	0
	0
	0

Details of re-assessment (where required by referral or deferral)

Original form of assessment	Form of re-assessment	ILOs re-assessed	Timescale for re-assessment
Essay (2,500 words)	Essay (2,500 words)	1, 3, 4, 5	August/September re-assessment period
Essay (2,500 words)	Essay (2,500 words)	1, 3, 4, 5	August/September re-assessment period
Technical report (2,500 words)	Technical report (2,500 words)	1, 2, 5, 6	August/September re-assessment period

Re-assessment notes

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to redo the assessment(s) as defined above. If you are successful on referral, your overall module mark will be capped at 50%.

Indicative learning resources - Basic reading

Bryson, J. (2021) ‘The Artificial Intelligence of the Ethics of Artificial Intelligence: An Introductory overview for Law and Regulation’in Dubber, M., Pasquale, F. & Das, S. (eds.) The Oxford Handbook of Ethics of AI Oxford: Oxford University Press.

Johnson, J. (2021) Artificial Intelligence and the Future of Warfare: The USA, China, and Strategic Stability Manchester: Manchester University Press.

Larranaga, M. & Smith, P. (2020) ‘Theoretical underpinnings of Homeland Security Technology’ in Ramsay, J., Cozine, K. & Comiskey, J. (eds.) Theoretical Foundations of Homeland Security: Strategies, Operations, and Structures London: Routledge

Mitchel, M (2019) Artificial Intelligence: A Guide for Thinking Humans London: Pelican Books

Rademacher, T. (2020) Artificial Intelligence and Law Enforcement in Wischmeyer, T. & Rademacher, T. (eds) Regulating Artificial Intelligence Cham: Springer https://doi.org/10.1007/978-3-030-32361-5_10

Steff, R., Burton, J. & Soare, S. (2022) Emerging Technologies and International Security: Machines, the State, and War London: Routledge

Stewart, H. (2022) ‘Digital transformation Security Challenges Journal of Computer Information Systems

https://doi.org/10.1080/08874417.2022.2115953

Williams, P. & Mc Donald, M. (eds.) (2023) Security Studies: An Introduction (4^th edition) London: Routledge.

Key words search

Security; Technology; Artificial intelligence; Defence; Law enforcement; Policing; Emerging technologies

Credit value	30
Module ECTS	15
Module co-requisites	SPAM003 Computational Social Science 1 and SPAM004 Computational Social Science 2
NQF level (module)	7
Available as distance learning?	No
Origin date	25/10/2023
Last revision date	07/02/2024