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CM50304: AI challenge

[Page last updated: 27 October 2020]

Follow this link for further information on academic years Academic Year: 2020/1
Further information on owning departmentsOwning Department/School: Department of Computer Science
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • Group Project (CW 100%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Description: Aims:

* To expose students to current challenges in artificial intelligence from academic, industrial, and social perspectives.
* To enable students to reach judgements in respect of accountability, responsibility and transparency about solutions to artificial intelligence challenges.
* To teach and to allow students to put into practice their developing awareness of ethical and related challenges in artificial intelligence in society.
* To give students experience of researching advanced topics in computer science, with a particular focus on artificial intelligence, exposure to the state of the art, undertaking a study and presenting the results.

Learning Outcomes:

* Demonstrate knowledge of computer science and artificial intelligence.
* Demonstrate knowledge of engineering and artificial intelligence.
* Demonstrate knowledge of social science and artificial intelligence.
* Show awareness of ethical issues in specifying and designing AI systems.
* Critical evaluation of the specification and design of AI systems.
* Recognise and critically analyse state of the art developments in AI.
* Specify, design, conduct and reflect upon original research into AI systems.


* Problem solving (T/F/A).
* Working with others (T/F/A).
* Working in (interdisciplinary) teams (T/F/A).
* Ability to reason analytically and scientifically (T/F/A).
* Ability to research, apply, present and argue about the state of the art in AI (T/F/A).
* Apply critical thinking and problem solving to a case study (F/A).
* Use appropriate evidence, and standards of logic and argumentation to support claims (F/A).
* Use appropriate standards of referencing, citations and presentation (F/A).

Course content will draw on a range of foundational, key historical and state of the art material across computer science, engineering and social science. Topics include:
* Effective team working.
* Different interpretations of accountability, responsibility and transparency in science, social science and society.
* Guest lectures on AI challenges from CDT partners (industry, NGOs, government and academics) and other external parties.
* Co-creation of responses to challenges with CDT partners and other external parties.
* Team project addressing one of the challenges identified, drawing on relevant literature, to specify, design and build a prototype solution and to reflect critically on the extent to which it meets accountability, responsibility and transparency concerns.
Further information on programme availabilityProgramme availability:

CM50304 is Compulsory on the following programmes:

Department of Computer Science
  • RSCM-AFM51 : Integrated PhD Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM51 : MRes Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM52 : MSc Accountable, Responsible and Transparent Artificial Intelligence


  • This unit catalogue is applicable for the 2020/21 academic year only. Students continuing their studies into 2021/22 and beyond should not assume that this unit will be available in future years in the format displayed here for 2020/21.
  • Programmes and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.