Module code: LAWM161

Module Overview

This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.

Module provider

School of Law

Module Leader

BARCZENTEWICZ Mikolaj (Schl of Law)

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: 117

Seminar Hours: 33

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Indicative content includes:
- Cases studies of major incidents illustrating ethical and regulatory problems in the field of artificial intelligence.
- How different ethical frameworks can be applied to think about the uses of artificial intelligence.
- Approaches to understanding the ethical (and regulatory) requirements of fairness, explainability and privacy (and how to translate them into technical specifications).
- Fundamental legal issues applied to artificial intelligence (eg liability, taxation, privacy, ownership).

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework 3000-word coursework 100

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate achievement of module learning outcomes identified above in respect of knowledge gained, critical/analytical ability and skills acquired. The assessment address all learning outcomes listed above.

Thus, the summative assessment for this module consists of:
- 3000 word coursework

Formative assessment and feedback
- 1500 word coursework.
- Individual and general feedback provided to students.
- Other formative exercises may be set in or outside class.

Module aims

  • Provide students with an understanding of the key ethical and regulatory issues associated with artificial intelligence.
  • Enable students to apply the methods of reasoning and analysis of those issues used in ethics and in law.
  • Expose students to the challenges of interdisciplinary thinking about artificial intelligence.

Learning outcomes

Attributes Developed
001 Demonstrate understanding of the basic ethical and regulatory issues associated with artificial intelligence. CK
002 Formulate and communicate their views of those issues in an interdisciplinary environment. CPT
003 Critically analyse statements on ethical and regulatory requirements associated with artificial intelligence. CKPT
004 Use and critically engage with academic sources related to ethics and regulation of artificial intelligence. CKPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

Seminars will expose students to the complexities of each topic, evaluating and examining key theories in more depth and through the application of knowledge to real and hypothetical scenarios. The teaching strategy is also designed to encourage independent study and research. Students will be provided with preliminary reading references but will be expected to undertake additional research into each topic under their own steam. During seminars students will be expected to demonstrate their ability to apply that research to discuss given ethical and regulatory problems, to demonstrate self-direction and originality in tackling and proposing solutions to such problems, and to evaluate critically current research and advanced scholarship in relevant areas.

The learning and teaching methods include: one 3-hour seminar per week (11 weeks).
The module delivery is supplemented by guidance provided via the SurreyLearn module area and consultation hours during the Semester.

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
Upon accessing the reading list, please search for the module using the module code: LAWM161

Other information


Programmes this module appears in

Programme Semester Classification Qualifying conditions
Artificial Intelligence MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module

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 2020/1 academic year.