AI AND LAW - 2022/3
Module code: LAWM164
This module explores the major legal and regulatory issues associated with the development and us use of artificial intelligence across various sectors, such as financial, healthcare, transportation, and military sectors. Artificial intelligence is considered as a broad discipline with the goal of creating intelligent machines that emulate and then exceed the full range of human cognition. The module will focus on various subsets of AI, such as machine learning, reinforcement learning, and deep learning and their respected legal and regulatory challenges based on real case studies and theoretical literature. Selected topics include:
¿ What are the major approaches regarding the regulation of AI systems and what are the consequences of them?
¿ What is the rationale for creating a new type of legal personality for AI systems?
¿ What applications of AI systems can constitute too excessive risk and why?
¿ What are the key standards for AI systems for both engineers and corporations?
¿ Do autonomous systems create responsibility or accountability gap?
¿ What is the concept of meaningful human control and how it is applicable to the use of autonomous systems?
¿ Do lethal autonomous weapon systems exist and how to regulate them?
¿ What are the legal challenges of data sharing partnerships and how to overcome them to build a proprietary value?
The focus of the module is to critically present the state of the art regarding the regulation of AI in major countries and discuss the key legal and regulatory problems associated with the use of AI. The module is based primarily on the case study method and cases are selected from business and policy profession from recent years. The module helps students develop their thinking on how to translate abstract legal and regulatory considerations such as responsibility, standards of care, or privacy concerns into practice.
School of Law
FIRLEJ 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
Lecture Hours: 33
Prerequisites / Co-requisites
Indicative content includes:
¿ Practical business and policy cases studies of major legal and regulatory problems in the field of artificial intelligence.
¿ Academic literature regarding the legal and regulatory concepts applicable to the development and use of artificial intelligence.
¿ Guest presentations from practitioners regarding specific legal and/or regulatory challenges associated with the development and use of artificial intelligence.
|Assessment type||Unit of assessment||Weighting|
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 addresses 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.
- ¿ Provide students with an understanding of the key legal and regulatory issues associated with artificial intelligence.
¿ Enable students to apply the methods of reasoning and analysis of those issues used in law.
¿ Expose students to the challenges of interdisciplinary thinking about artificial intelligence.
|001||Demonstrate understanding of the basic legal and regulatory issues associated with artificial intelligence.||CKT|
|002||Formulate and communicate their views of those issues in an interdisciplinary environment.||CPT|
|003||Critically analyse statements on legal and regulatory requirements associated with artificial intelligence||CKPT|
|004||Use and critically engage with academic sources related to law and regulation of artificial intelligence||CKPT|
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.
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.
Upon accessing the reading list, please search for the module using the module code: LAWM164
Reading list to be provided at the start of each term
Programmes this module appears in
|Artificial Intelligence MSc||2||Optional||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 2022/3 academic year.