Security, Society and Algorithms

Module title	Security, Society and Algorithms
Module code	SOC3144
Academic year	2023/4
Credits	15
Module staff	Dr Lewys Brace (Convenor)

Duration: Term	1	2	3
Duration: Weeks		11

Number students taking module (anticipated)	30

Module description

As advancements in Artificial Intelligence (AI) fundamentally change how societies function, both novel security challenges and ethical, legal, and socio-political questions are posed. While this technology has been employed in areas such as homeland security and crime prevention to protect individuals, assets, and sensitive data, the same technology can or has been used by malicious actors, such as extremist groups and other nation states, to cause a range of social harms. This module will introduce you to AI, its impact in the security context, 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 aim is to provide you with a broad understanding of the nature of artificial intelligence (AI) in the context of security and law enforcement, and the subsequent impact of this on individuals and society. It will look at how these technologies have been utilised by law enforcement and state actors, but will also explore how smaller/unfriendly states, non-state actors, and other groups are adapting to these technologies and utilising them for malicious purposes. Developing your understanding of these issues will involve you learning about the use of AI by police forces to detect and prevent crime, the nature of adversarial AI, how AI is used by extremist groups, the role of AI in modern warfare, building resilience, the governance and ethical issues surrounding AI, and other related subjects. The second aim involves you developing the skills necessary to implement a very basic AI algorithm. These skills will be developed during practical-based lab sessions and will involve you learning the generic programming language Python to a basic level. No prior experience with coding will be necessary as the module will provide you with an introduction to coding and the Python syntax, before going on to show how to use it to build an AI implementation of your choosing.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

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

1. Demonstrate a strong knowledge of artificial intelligence (AI) in various security and threat contexts.
2. Demonstrate a good level of confidence in using the generic programming language Python to implement a basic AI system.
3. Demonstrate a good understanding of how an AI algorithm actually works and its impact and associated ethical implications.

ILO: Discipline-specific skills

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

4. Critically reflect on the role and impact of AI within the wider context of society more generally
5. Demonstrate an understanding of the role AI could play in future society and its impact
6. 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...

7. Work as part of a group on a joint project
8. Demonstrate written analytical skills by producing an essay and technical report to a deadline
9. Present group-based work in a professional 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)?
Security challenges in the digital age and their societal impact/how has AI changed the security landscape?
AI and data
The ethics of AI
Adversarial AI
Law enforcement and AI in crime detection and prevention.
AI and domestic/homeland security issues in wider society
AI and extremism
AI and the state
AI, war, and the international system
Building resilience
AI governance and law
The future of the relationship between AI, 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
23	127

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Learning and Teaching Activities	11	11 x one-hour lectures
Scheduled Learning and Teaching Activities	12	6 x two-hour labs
Guided Independent Study	36	Course readings and coding practice
Guided Independent Study	25	Reading/research for essay
Guided Independent Study	73	Group work/research and coding for technical report

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Presentation on practical work	5 minutes	2-3, 7, 9	Verbal/written 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	30	1,750 words	1, 4-6, 8	Written feedback
Technical report	70	1,750 words	1-3, 5, 7, 8-9	Written feedback

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	Essay (1,750 words) (30%)	1, 4-6, 8	Referral/Deferral period
Technical report	Technical report (1,750 words) (70%)	1-3, 5, 7, 8-9	Referral/Deferral 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 40%) 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 40%.

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.

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

McLevey, J. (2022) Doing Computational Social Science: A Practical Introduction London: Sage.

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

Indicative learning resources - Web based and electronic resources

ELE – College to provide hyperlink to appropriate pages

Key words search

Artificial intelligence, AI, Security, Law enforcement, Python, Machine learning

Credit value	15
Module ECTS	15
Module pre-requisites	None
Module co-requisites	None
NQF level (module)	6
Available as distance learning?	No
Origin date	17/01/2023
Last revision date	06/03/2023