MSc Artificial Intelligence and Ethics Programme Specification

Programme Summary

The MSc Artificial Intelligence and Ethics is a well-integrated programme of study with a targeted focus on Artificial Intelligence (AI), both in theory and in application. The MSc Artificial Intelligence and Ethics is an in-depth computer science programme, intended for graduates of a wide range of disciplines, and presupposing no background in computing, it ensures students are equipped with relevant knowledge and skills, covering not only recent technical developments, but also broader ethical and theoretical considerations.

The programme allows students to progressively develop their understanding of the techniques of data science, machine learning, and natural language processing, alongside key concepts and methods of computer science, while honing their programming skills in, e.g., Python and Java; and to simultaneously refine their thinking and communication skills, through humanities courses devoted to a consideration of key issues, both practical and theoretical, arising in connection with AI.

Programme Integration

The programme comprises taught courses, totaling 120 credits, as well as a 60-credit MSc dissertation project. The coursework covers two subjects: artificial intelligence and ethics.

Before students embark on the study of the MSc programme, they are able to engage in an introductory, non-credit bearing course (i.e., a bootcamp or taster course) on programming and mathematics fundamentals. The Introduction to Programming taster course is accessible to all MSc students pursuing computer science and technology-related programmes at Level 7. Students are strongly advised to avail themselves of this course, as it serves as a crucial foundation for acquiring essential programming and mathematical skills. Introduction to Programming encompasses a comprehensive understanding of software development principles, problem-solving techniques, data representations, control flow, probability, calculus, and the construction of computer programs. Some students may find it necessary to enhance their mathematical or programming proficiencies prior to embarking on their MSc programme, while others may require a refresher to align their knowledge and understanding with the teaching standards at Northeastern University – London. The course is available online through our Virtual Learning Environment prior to starting the main MSc programme.

Six 15-credit computing courses teach students the theory and application of computer and data science, especially in relation to Artificial Intelligence (AI). In their first semester, students learn the basics of programming (e.g., if-then-else statements, for loops and data collections), alongside the fundamentals of computing (e.g., logic operators, algorithm complexity and statistics, respectively), how to ingest and transform data (e.g., numerical arrays, images, or text), and how to design data driven applications ethically. In their second semester, students learn to develop machine learning applications at breadth and depth. Furthermore, we choose Natural Language Processing with Deep Learning to study in depth because it has a profound technical and societal impact nowadays and it is pertinent to humanics.

One 30-credit flagship course teaches students to think carefully and communicate clearly about responsible engineering practices arising in relation to computing, data usage, AI, and other emerging technologies.

The 60-credit individual project is a sustained piece of independent work on an agreed topic of the student’s choice, in line with the programme focus. It runs throughout the year so that students have ample time to focus their independent learning with the right guidance by their supervisor(s).

The creation, implementation, and effects of Artificial Intelligence (AI) systems all have an ethical dimension. As AI develops and integrates into more facets of our lives, it is crucial to make sure that its application adheres to ethical standards and considerations. These considerations will be addressed throughout the programme by using seminar discussions, forums, literature and teacher guidance on the implications of AI implementation and adoption. It will also be specifically addressed in the core AI ethics course. Students on this programme will be able to understand and communicate the accountability, transparency and explainability of AI, as well as will be able to comprehend how AI systems make decisions and their impact. In addition, by the end of the programme, students will be able to understand ethical AI use and take into account potential social, economic, and environmental implications with the goal of advancing society as a whole rather than just a chosen few.

The programme is designed and delivered so as to integrate the above component parts into a whole that ensures students graduate with both a technical and theoretical understanding of AI and its applications, as well as a broad, contextual appreciation of its implications.

Full Time Programme Structure

Semester One

LCSCI7235 Fundamentals of Computation, Data, and Algorithms (15 credits)

LDSCI7234 Programming for Data Applications (15 credits)

LPHIL7252 AI and Data Ethics (15 credits)

LDSCI7236 Theory and Applications of Data Analytics (15 credits)

Semester Two

LDSCI7230 Advanced Topics in Responsible AI (30 credits)

LDSCI7227 Machine Learning (15 credits)

LDSCI7226 Natural Language Processing with Deep Learning (15 credits)

Semester Three

LDSCI7237 Artificial Intelligence Dissertation Project (60 credits)

Part Time Programme Structure

Year One

Semester One

LCSCI7235 Fundamentals of Computation, Data, and Algorithms (15 credits)

LPHIL7252 AI & Data Ethics (15 credits)

Semester Two

LDSCI7230 Advanced Topics in Responsible AI (30 credits)

Semester Three

Begin LDSCI7237 Artificial Intelligence Dissertation Project (60 credits)

Year Two

Semester One

LDSCI7234 Programming for Data Applications (15 credits)

LDSCI7236 Theory and Applications of Data Analytics (15 credits)

Semester Two

LDSCI7227 Machine Learning (15 credits)

LDSCI7226 Natural Language Processing with Deep Learning (15 credits)

Semester Three

Complete LDSCI7237 Artificial Intelligence Dissertation Project (60 credits)s)

Entrance Requirements

Entry requirements – our typical offer for postgraduate study is an upper second-class honours undergraduate degree (or the equivalent) in an academic subject such as Economics, English, History, Languages, Philosophy, Politics, Sociology, Psychology; but each applicant will be assessed on an individual basis, including relevant professional experience where applicable. If English is not an applicant’s native language, they will need to demonstrate proficiency in English in order to study at the University. For a list of equivalencies, please check here.

Recognition of Prior Learning

Where a student wishes to apply for the recognition of prior learning on the basis of certificated or experiential learning, they should follow the University’s Recognition of Prior Learning and Credit Transfer Policy.

Aims of the Programme

The programme aims to:

• Produce graduates who are proficient in the design and implementation of data-oriented and machine learning applications using state-of-art software libraries, techniques and algorithms.
• Build strong foundations for understanding the data science techniques that underpin recent advances in machine learning and, in particular, natural language processing.
• Develop a critical understanding of how philosophical thinking can contribute to the beneficial development of AI and ethical use of data, engaging with related concepts, theories and arguments in the field.

Learning Outcomes

Knowledge and Understanding

A student will be able to: 

Subject Specific Skills

A student will be able to: 

Transferable and Professional Skills

A student will be able to: 

All of the above learning outcomes are mapped to the relevant QAA Subject Benchmark threshold statements in Appendix C. For the exit awards see Appendix A.

Map of Courses to Programme Learning Outcomes

Teaching and Learning Strategies

Teaching Methods

• Lectures and seminars
• Lab sessions
• Student presentations
• Collaborative group work
• Individual essay-based tutorials
• Feedback on formative essays
• Feedback on coding assignments and accompanying technical reports
• Office hours
• Online discussion forums
• Dissertation project supervisions (which support both written and oral communication skills)
• (Structured) independent study and research

The University teaches in small groups and is committed to providing individual attention and guidance. Lectures and seminars always include student interaction and dialogue. Each student will receive approximately 220 contact hours: 124 hours of lectures and seminars, 90 hours of lab sessions, and 8 hours of individual tutorials and supervisions. A further 120 office hours are available to all students to arrange personalised tutorials or discuss other matters for computing courses. As indicated below, students can participate in the research seminars (e.g. the meetings of the Cognitive Science Research Group) and, with the relevant faculty members’ permission, audit other lectures and seminars of their choice. Assessment, as indicated above, is in a variety of modes: coursework essay, coding and/or written assignment, oral presentation with PowerPoint or handout, and dissertation with viva.

Learning Opportunities

Students may wish to attend the regular meetings of the University Cognitive Science Research Group, in which issues in Computer Science, Philosophy, and Psychology are discussed; and they may also be able to participate in Northeastern’s online Information Ethics Roundtable.

Inclusive Teaching and Learning

The faculty are deeply committed to widening participation in Artificial Intelligence, both through outreach activities and through a teaching environment that is inclusive towards a variety of backgrounds and learning styles.
The University is a part of the global network of Northeastern University, home to the Center for Inclusive Computing.
Members of the faculty are much engaged in the public dissemination of their discipline, visiting a wide range of schools, hosting open lectures, engaging with the media, and publishing in accessible formats.
The high staff-student ratio at the University is especially important to the faculty’s ability to give individualised attention to students, and thus to be inclusive towards a variety of backgrounds and learning styles. The faculty facilitates a wide range of academic and social events in which academics and students are brought together.
The University will make reasonable adjustments for students with disabilities, in accordance with the recommendations of Student Support and Development. Where necessary, following consultation with Student Support and Development, alternative forms of assessment may be offered.
The variety of modes of assessment in this programme may render it more inclusive than those which assess in more uniform ways.

E-Learning

The University ensures students are supported outside of class contact time by means of a virtual learning environment, through which students access learning materials and communicate with fellow students and faculty. Students can additionally access past faculty lecture videos and general study information, on such topics as time-management skills and how to read effectively. Academic writing support is also available.

Research-Led Practice-Driven Teaching

All of the University’s faculty have been recruited on the basis of their research activity, as well as their talents in teaching, and are encouraged to remain active in their research field, partly by being given an individual annual research budget and regular sabbatical leave. The teaching has been developed and allocated on the basis of research interests and expertise. The faculty are committed to supporting a lively, open, and interactive teaching environment, in which research and teaching are mutually complementary.

Assessment

Assessment Method

• Set exercises (including coding)
• Written assignments (including essays and coding)
• Dissertation
• Oral presentation

Appendix B is the programme structure and assessment summary.

Assessment Regulations

The University’s Assessment Regulations can be found here.

Student Support

Disabilities and/or Specific Learning Difficulties (SPLDS)

Students are strongly encouraged to inform the University of any medical conditions, disabilities, specific learning difficulties (SpLD) or neurological differences as soon as is practical. Students will be asked to submit supporting documentation from a doctor, clinical or educational psychologist detailing the nature of their disability and the impact it is likely to have on their studies in order to help us put in place appropriate support and accommodations. More information can be found in the Student Disability Policy here Student Disability Policy. This data is managed and securely stored by Student Support and Development (SSD). During Welcome Week, a number of talks and events are held which are designed to support and inform students with regard to mental health, disabilities, safety and learning support.
SSD meet with students as soon as possible, and preferably before the start of the academic year, to discuss their needs and draft a Learning Support Plan (LSP) which outlines the support to be provided both within the University (if appropriate) and externally. If requested by the student, the SDD will then arrange to inform relevant faculty of the student’s needs and any reasonable adjustments required.
If a student is undiagnosed but believes they may have a SpLDS (e.g. Dyslexia) SDD will help them to access diagnostic services. If the assessment confirms a SpLDS, SDD will work the student in preparing a LSP and will provide advice about accessing additional funding and support through the Disabled Students Allowance, where a student may be eligible.
For more information, please click here.

Employability Skills

As Artificial Intelligence (AI) and its applications become increasingly prevalent in society, there is a growing recognition across a range of sectors of: (i) the need to integrate ethics in the field; and (ii) the value of diverse and interdisciplinary thinking in the field’s development. The MSc Responsible AI programme teaches students a range of highly employable technical skills while answering to these needs:

• Programming skills: deliver original, technically sound software solutions to data-oriented problems using appropriate software development and machine learning methods and techniques that adhere to best practices and industry standards.
• Leadership skills: work independently and to deadlines; research related work and synthesise it creatively; and then engage with peers to critically assess a data-driven problem and provide constructive feedback on the design, management, and evaluation of a solution.
• Communication skills: present, orally or in writing, technical solutions, findings, and opinions on theoretical, societal and ethical implications of AI applications in a clear and structured manner to both technical and non-technical audiences.

Careers Education, Information and Guidance

Master’s students will have access to the University’s Careers Advisory Service. This includes employer receptions with representatives from a wide range of sectors and our electronic Careers Centre, containing features and functionality for careers guidance, interview advice and job searching.
In addition, Careers Advisers, supplemented with support from tutors, offer advice, often one-to-one, on securing a professional future tailored to students’ skills and ambitions.

Quality Evaluation and Enhancement

Award Standards

Every programme of study is developed by the Faculties, utilising their subject specialists and approved by the University’s Academic Board.

Review and Evaluation Mechanisms

The University has robust procedures, as described in AQF4 Programme and Course Approval and Modifications and AQF5 Annual Monitoring and Reporting, in place to assure the quality of the programme development, delivery, management, systematic monitoring and ongoing review and enhancement of all University programmes. Enhancements are made as necessary to ensure that systems remain effective and rigorous.
The University utilises constructive feedback from a variety of sources, internal and external, to inform its decision-making process to enhance the programme and student experiences. These feedback sources are listed below:
Annual Course Reviews, written by the Course Leader, are prepared to enable the Course Leader to reflect on the course, using a variety of data and student/faculty feedback to enhance the course and support the writing of the Annual Programme Review.
Annual Programme Reviews, written at the end of each academic year, are prepared in order to enhance individual programmes and to plan ahead.
Annual External Examiner reports are prepared by the independent External Examiners, as appointed by the University, to confirm that a programme has been assessed in accordance with the approved documentation and that the student performance meets the appropriate academic standards.
Formal student feedback mechanisms consist of student representatives attending meetings; course satisfaction surveys; and annual programme satisfaction surveys.
Informal student feedback is also valued by the University and this can take the form of students talking or corresponding with faculty or professional staff.

Version History

Disclaimer 

The University has checked the information provided in this Programme Specification and will aim to deliver this programme in keeping with this Programme Specification. However, changes to the programme may sometimes be required arising from annual monitoring, student feedback, and the review and update of courses and programmes. Where this activity leads to significant changes to courses and programmes there will be prior consultation with students and others, wherever possible, and the University will take all reasonable steps to minimise disruption to students. It is also possible that the University may not be able to offer a course or programme for reasons outside of its control, for example, due to the absence of a member of staff or low student registration numbers. Where this is the case, the University will aim to inform applicants and students as soon as possible, and where appropriate, will facilitate the transfer of affected students to another suitable programme. 

Copyright 

The contents of this Programme Specification are the copyright of the University and all rights are reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, such as electronic, mechanical, photocopied, recorded or otherwise, without the prior consent of the University.

Appendix A – Exit Awards

Postgraduate Certificate

60 credits

Postgraduate Diploma

120 credits

Appendix B – Programme Structure And Assessment Summary

COURSE TYPE: C = Compulsory; O = Option.

COURSE MODE: CD = Campus Delivery; BK = Block Delivery; BL = Blended Learning; DL = Distance Learning and Self-Directed Learning; EL = E-Learning; EX = Experiential; PL = Placement; WB = Work Based Learning,

ASSESSMENT WEIGHTING: AE1 = Assessment Element 1; AE2 = Assessment Element 2; AE3 = Assessment Element 3;
AE4 = Assessment Element 4

Appendix C – Map To Qaa Subject Benchmark Computing (Master’s)