MA Philosophy and Artificial Intelligence Programme Specification

Programme Summary

The MA Philosophy and Artificial Intelligence is a well-integrated programme of study with a targeted focus on philosophical issues surrounding computing, data, information, and artificial intelligence. Presupposing no background in either philosophy or programming, it ensures students are equipped with the skills and knowledge needed for graduate study in philosophy, and offers opportunities to acquire relevant coding and data science techniques. It gives students the philosophical tools to address the social, ethical, and political problems raised by artificial intelligence. The programme allows for a progressive exploration of ethical, epistemological, and metaphysical issues arising in this area. Graduates of the programme will be well-prepared to use their philosophical knowledge and skills to contribute to both the theoretical and practical development of artificial intelligence and its applications.

Full Time Programme Structure

Semester One

LPHIL7251 Mind and Reality (15 credits)

LPHIL7252 Artificial Intelligence and Data Ethics (15 credits)

First course of first pathway (15 credits)

First course of second pathway (15 credits)

Semester Two

LPHIL7253 Values and Society (15 credits)

LPHIL7254 Minds and Machines (15 credits)

Second course of first pathway (15 credits)

Second course of second pathway (15 credits)

Semester Three

Either

LPHIL7262 Philosophy Master’s Dissertation (60 credits) or

LPHIL7261 Philosophy Project-Based Dissertation (60 credits)

Part Time Programme Structure

Year 1

Semester One

LPHIL7251 Mind and Reality (15 credits)

LPHIL7252 Artificial Intelligence and Data Ethics (15 credits)

Semester Two

LPHIL7253 Values and Society (15 credits)

LPHIL7254 Minds and Machines (15 credits)

Semester Three

Start one of:

LPHIL7262 Philosophy Master’s Dissertation (60 credits)

LPHIL7261 Philosophy Project-Based Dissertation (60 credits)

Year 2

Semester One

First course of first pathway (15 credits)

First course of second pathway (15 credits)

Semester Two

Second course of first pathway (15 credits)

Second course of second pathway (15 credits)

Semester Three

Continue Dissertation (60 credits)

Programme Integration

The programme comprises both compulsory and optional courses (totaling 120 credits), as well as a (60 credit) dissertation. Some of the required research methods courses (Mind and Reality, and Values and Society – 15 credits each) provide the knowledge and skills that any graduate student in philosophy needs to master. Others (Artificial Intelligence and Data Ethics, and Minds and Machines – 15 credits each) ensure students have a strong grasp of central philosophical issues arising specifically in relation to computing, data, and artificial intelligence.

Students must choose TWO of the following four optional pathways:

1. Programming
2. Technology, Artificial Intelligence, and Human Values
3. Philosophical Foundations
4. Ethical Foundations

The pathways allow students to specialise in the areas that are of most interest to them. The programming pathway gives students practical experience in analysing data, programming, and employing artificial intelligence models. The Technology, Artificial Intelligence, and Human Values pathway allows students to gain a broader understanding of issues within the philosophy of artificial intelligence and technology. The Philosophical Foundations pathway allows students to directly study the main foundational epistemological, metaphysical, and logical issues that feed into philosophical debates on artificial intelligence. The Ethical Foundations pathway allows students to study foundational concepts in ethics, political theory, and the history of philosophy. This will give them a deep knowledge of concepts of political theory, ethics, and the history of philosophy, enabling students to apply them to debates over the ethics of AI.

Each pathway is split into two one-semester courses worth 15 credits each. 

Programming (30 credits)

Before students embark on this pathway, they will take an introductory, non-credit bearing course (i.e. a bootcamp or taster course) on programming and mathematics fundamentals.

Semester One

LDSCI7234  Programming for Data Applications (15 credits)

Semester Two

LDSCI7236 Theory and Applications of Data Analytics  (15 credits)

Technology, Artificial Intelligence, and Human Values (30 credits)

Semester One

LPHIL7255 Technology and Human Values (15 credits)

Semester Two

LPHIL7260 Responsible Artificial Intelligence (15 credits)

Philosophical Foundations (30 credits)

Semester One

LPHIL7256 Rationality and Reasoning (15 credits)

Semester Two

LPHIL7257 Mind, Meaning, and Metaphysics (15 credits)

Ethical Foundations (30 credits

Semester One

LPHIL7258 Political and Ethical Ideas Across History (15 credits)

Semester Two

LPHIL7259 Advanced Topics in Ethics and Political Theory (15 credits)

Dissertation (60 credits)

Students have the opportunity to choose one of two different models of dissertation project: a dissertation consisting of an extended philosophical essay or a “project-based dissertation”. The latter allows students to embark on a more applied and/or interdisciplinary project which does not fit the model of a typical philosophy dissertation. Students will be assessed on the way they have gone about the project and their reflections on it, as well as on the output of the project itself.

NB: Pathway courses will be subject to availability, which is to be determined by a combination of student demand and faculty requirements.

Entrance Requirements

The 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, taking into account 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:

• Provide a strong foundation in relation to key issues in the philosophy of data and artificial intelligence.
• Develop students’ critical engagement with the pertinent concepts, theories and arguments in this field.
• Enable students to form, elaborate and defend their own views in the field.
• If relevant options are chosen, build a basis for understanding some of the techniques of data science that underpin recent advances in machine learning and artificial intelligence.
• Develop an understanding of how philosophical thinking can contribute to the beneficial development of artificial intelligence.

The overall aim of the programme is to:

• Provide a teaching and learning environment which achieves the above aims by enabling students to demonstrate the learning outcomes below.

Learning Outcomes

Knowledge and Understanding

On successful completion of the programme, a student will be able to:

Subject-Specific Skills

Transferable and Professional Skills

On successful completion of the programme, a student will be able to:

For the exit awards see Appendix A.

Teaching and Learning Strategies

Features of the teaching and learning environment:

• The University’s campus has state-of-the-art teaching rooms and independent and group study spaces.
• A Virtual Learning Environment (VLE) for each course with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assessment briefs, slides or handouts, discussion boards, and sample examination papers and examiners’ reports) to orientate and engage students in their studies.
• Northeastern University’s online library digital resources, and other online academic resources, such as JSTOR and the OED. Students are inducted on their use at the start of the programme, and wider digital literacy is reinforced and developed across their studies.
• Students can also apply for a reader’s card to use the British Library membership and apply for membership of any of the City of London libraries. Students at the University can apply for Senate House Library membership.
• As part of the wider teaching and learning environment, the University hosts a range of academic and social events in which students, faculty, alumni and interlocutors from outside the academy are brought together.

Teaching Methods

• Lectures/seminars
• Presentations
• Collaborative group work
• Written feedback on formative essays
• (Mock) examination
• Labs (for data science options)
• Office hours
• Online discussion forums
• Individual dissertation supervisions (which support both written and oral communication skills)
• (Structured) independent study and research

The University is committed to providing individual attention and guidance. Seminars include student interaction and dialogue. Faculty also hold regular ‘Office Hours’, which are opportunities for students to explore ideas, raise questions, or seek targeted guidance or verbal feedback on a one-to-one basis.

Assessment, as indicated below, is in a variety of modes: examinations (both open-book/in-tray style and closed-book), written assignments (reports) involving a range of required elements, oral presentations, and a final project with a viva-style presentation.

Learning Opportunities

The optional regular research seminars – both the Philosophy Research Seminar, which is often combined with the student-run Philosophical Society, and the meetings of the Cognitive Science Research Group – offer a lively and varied menu of talks and discussions involving both internal and invited speakers. MA Philosophy and Artificial Intelligence students are invited and encouraged to attend these.

Students will also be encouraged to attend the broad programme of liberal-arts professorial lectures at the University given by our visiting professors.

Inclusive Teaching and Learning

The University is deeply committed to widening participation in Philosophy and Artificial Intelligence and their intersection, both through outreach activities and through a teaching environment that is inclusive towards a variety of backgrounds and learning styles. 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 Faculty is committed to giving individualised attention to students, and thus 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 is able to make reasonable adjustments for students with disabilities. Student Support and Development (SSD) are able to put into place individual Learning Support Plans, which set out what additional support and reasonable adjustments can be provided based on an assessment a disabled student’s needs. Applicants with a disability are encouraged to contact SSD as early as possible to discuss their support needs and the adjustments and support available at the University.

The University provides mandatory training in Diversity, Equity and Inclusion for all staff and students. This is an important part of our support for a diverse and inclusive experience for all members of our organisation across teaching, research and all of our University processes, procedures and life.

Research-Led Practice-Driven Teaching

The teaching has been developed and allocated on the basis of research interests and expertise. The University is committed to supporting a lively, open, and interactive teaching environment, in which research and teaching are mutually complementary

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 general study information, on such topics as time-management skills and how to read effectively.

Experiential Learning

The University is a global, experiential, research university built on a tradition of engagement with the wider world. Experiential learning is integral to our teaching and learning. The University offers a wide range of experiential learning opportunities. Some are integrated into the classroom (curricular), some are designed to complement classroom learning (co-curricular), and some are independent of any course (extra-curricular). Because it is grounded in real-world experience, experiential learning can also take place outside of the university.

Assessment

Assessment Methods

• Examination
• Oral Presentation
• Coding and/or written coursework assignments (including essays)
• Dissertation
• Viva voce

Appendix C is the programme structure and assessment summary.

Assessment Regulations

The University’s AQF 7 Academic 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. 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) the SDD will help them to access diagnostic services. If the assessment confirms a SpLDS, the SDD will work with 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 and its applications become increasingly prevalent in society, there is a growing recognition, both within the industry and beyond, of the need to integrate ethics in the field, and of the value of diverse and interdisciplinary thinking in its development. The MA Philosophy and Artificial Intelligence teaches a range of highly employable skills that respond to these needs. In particular, the study of Philosophy at MA level cultivates skills that are employable across a range of sectors. These include the abilities to:

• Work independently, creatively, and to deadlines.
• Conduct research and explore relevant existing knowledge.
• Analyse, contextualise, and interpret complex ideas and materials.
• Synthesise and evaluate information against a backdrop of uncertainty.
• Solve problems through logical reasoning.
• Present findings and opinions in a clear, structured manner, whether orally or in writing.
• Engage in collaborative and constructive discussion.

Students who pursue the data science options will also be able to:

• Write computer programs.
• Provide quantitative and qualitative analysis of a given data set.

Above all, graduates of the MA Philosophy and Artificial Intelligence programme will be able to:

• Think and communicate clearly about data, information processing, and artificial intelligence, and their theoretical, societal, and ethical implications.

Careers Education, Information and Guidance

Masters 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 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 course questionnaires and Student Engagement Committee meetings at least once each semester, as well as annual student 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 endeavour 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 endeavour 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

4 x 15 credit Level 7 courses = 60 credits

Postgraduate Diploma

8 x 15 credit level 7 courses = 120 credits

Appendix B – Map of Courses to Programme Learning Outcomes

Appendix C – Programme Structure and Assessment Summary

Course Type:   R = Required; 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

Assessment Activity Type                                Code 

Written exam                                                              Exam

Take home exam                                                        TEx

Written assignment                                                     A

Report                                                                        R

Dissertation                                                                Diss

Portfolio                                                                      F

Project output (other than dissertation)                        P

Oral assessment and presentation                              Oral

Practical skills assessment                                          Pract

Set exercise                                                                Set