Overview
AI ethics degree. MSc Artificial Intelligence Ethics at Durham University: 6 assessed courses, 180 credits and 1,800 notional learning hours, at 10 hours per credit. The hours on each course add up to that total. Seminars run online. A Durham collegiate campus route is published separately from this online award.
MSc Artificial Intelligence Ethics is taught as one award, not a list of unrelated modules. The published length is 18 months, and the credit total is 180. Students move from Where a model helps and where it does not through Where a model helps and where it does not; Data for a model; Evaluation; Use in a workflow; Governance and the limit of the award; Capstone: applied AI file, and finish on Capstone: applied AI file.
Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation.
The award is built so that a graduate can do the following in practice: Produce a professional file in Artificial Intelligence Ethics Choose a method and write what the evidence cannot support Keep a record a third party can review
Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass.
The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.
Each credit is 10 notional learning hours. The courses add up to 180 credits and 1,800 hours.
Who This Programme Is For
- ✦People already working around Artificial Intelligence Ethics
- ✦Career-changers who can study without leaving their city
- ✦Teams who need a file they can review, not a slogan
Faculty spotlight
Durham University supervises the courses and the final assessed file. Supervision is assigned from the school that owns the award.
Programme Structure & Delivery
Coursework
Interactive modules, weekly readings, recorded and live seminars, and collaborative discussions with peers worldwide.
Assessment
Module assignments, proctored examinations, and capstone projects designed to demonstrate mastery of the field.
Learning Outcomes
- ✦Produce a professional file in Artificial Intelligence Ethics
- ✦Choose a method and write what the evidence cannot support
- ✦Keep a record a third party can review
- ✦State the limit of the award
- ✦Submit the final project inside the published hours
Career Pathways
- ✦AI product analyst
- ✦Applied machine-learning assistant
- ✦Automation lead
- ✦Model-risk coordinator
- ✦AI operations officer
Assessment
Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.
Curriculum
Stage 1 — 900 hours
SCLB901Where a model helps and where it does not- SCLB901-M1 · The question, the unit of analysis, and data quality — Where a model helps and where it does not
- SCLB901-M2 · Features, baselines, and error — Where a model helps and where it does not
- SCLB901-M3 · Validation, leakage, and what the model must not decide — Where a model helps and where it does not
- SCLB901-M4 · Monitoring, drift, and human review — Where a model helps and where it does not
- SCLB901-M5 · Interpretation for a non-technical decision maker — Where a model helps and where it does not
- SCLB901-M6 · A supervised project with method, result, and limitation — Where a model helps and where it does not
30 creditsSDFA202Data for a model- SDFA202-M1 · The question, the unit of analysis, and data quality — Data for a model
- SDFA202-M2 · Features, baselines, and error — Data for a model
- SDFA202-M3 · Validation, leakage, and what the model must not decide — Data for a model
- SDFA202-M4 · Monitoring, drift, and human review — Data for a model
- SDFA202-M5 · Interpretation for a non-technical decision maker — Data for a model
- SDFA202-M6 · A supervised project with method, result, and limitation — Data for a model
30 creditsSD5AG03Evaluation- SD5AG03-M1 · The question, the unit of analysis, and data quality — Evaluation
- SD5AG03-M2 · Features, baselines, and error — Evaluation
- SD5AG03-M3 · Validation, leakage, and what the model must not decide — Evaluation
- SD5AG03-M4 · Monitoring, drift, and human review — Evaluation
- SD5AG03-M5 · Interpretation for a non-technical decision maker — Evaluation
- SD5AG03-M6 · A supervised project with method, result, and limitation — Evaluation
30 creditsStage 2 — 900 hours
SBRCG04Use in a workflow- SBRCG04-M1 · The question, the unit of analysis, and data quality — Use in a workflow
- SBRCG04-M2 · Features, baselines, and error — Use in a workflow
- SBRCG04-M3 · Validation, leakage, and what the model must not decide — Use in a workflow
- SBRCG04-M4 · Monitoring, drift, and human review — Use in a workflow
- SBRCG04-M5 · Interpretation for a non-technical decision maker — Use in a workflow
- SBRCG04-M6 · A supervised project with method, result, and limitation — Use in a workflow
30 creditsSBHCU05Governance and the limit of the award- SBHCU05-M1 · The question, the unit of analysis, and data quality — Governance and the limit of the award
- SBHCU05-M2 · Features, baselines, and error — Governance and the limit of the award
- SBHCU05-M3 · Validation, leakage, and what the model must not decide — Governance and the limit of the award
- SBHCU05-M4 · Monitoring, drift, and human review — Governance and the limit of the award
- SBHCU05-M5 · Interpretation for a non-technical decision maker — Governance and the limit of the award
- SBHCU05-M6 · A supervised project with method, result, and limitation — Governance and the limit of the award
30 creditsSCBBN06Capstone: applied AI file- SCBBN06-M1 · The question, the unit of analysis, and data quality — Capstone: applied AI file
- SCBBN06-M2 · Features, baselines, and error — Capstone: applied AI file
- SCBBN06-M3 · Validation, leakage, and what the model must not decide — Capstone: applied AI file
- SCBBN06-M4 · Monitoring, drift, and human review — Capstone: applied AI file
- SCBBN06-M5 · Interpretation for a non-technical decision maker — Capstone: applied AI file
- SCBBN06-M6 · A supervised project with method, result, and limitation — Capstone: applied AI file
30 creditsAdmission requirements
Academic entry
- A bachelor's degree from a recognised institution, or equivalent professional standing considered by admissions
Documents
- A statement of about 500 words on why this award fits your work
- Two references, academic or professional
- A CV
Programme questions
How long is the MSc Artificial Intelligence Ethics?+
The published duration is 18 months. Teaching language: English. Actual time-to-complete depends on mode and any recognised prior learning.
Can I study this from my country?+
This is a fully online award. You study from your country. No student visa and no campus relocation are required.
What does it cost?+
Degree tuition for this award is published as £0 / tuition-free on the online pathway. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.
What are the entry requirements?+
Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.
How is the programme assessed?+
Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.
Will this award be recognised where I live?+
Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. Durham University publishes verification pages for certificates. We do not claim automatic equivalence in every country.
How do I apply?+
Start an application on this website. Progress is saved from the first step. Admissions: admissions@durhm.co.uk.
