DSC3003 : Responsible and Trustworthy Data and AI
- Inactive for Year: 2026/27
- Module Leader(s): Dr Tejal Shah
- Lecturer: Dr Mujeeb Ahmed, Dr Varun Ojha
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module develops your understanding of the responsible design, evaluation, and deployment of data science and artificial intelligence systems. You will study ethical, societal, technical, and regulatory issues associated with modern AI systems, including safety, explainability, security, fairness, robustness, and environmental impact, alongside frameworks and approaches that support trustworthy and responsible AI practice. The emphasis is on critically evaluating AI systems, understanding trade-offs and limitations, and making informed and responsible decisions about the design, deployment, and use of data-driven and intelligent technologies within professional and real-world contexts.
Outline Of Syllabus
Topics covered by this module include:
Fundamental concepts of AI safety, trustworthiness, fairness, robustness, and accountability
Principles and frameworks for responsible innovation and trustworthy AI
Technical and societal challenges associated with AI safety
Ethical, legal, and regulatory frameworks for AI systems
Safety assurance, evaluation, and risk assessment methods
AI-assisted software development and engineering practices
Safe coding patterns and responsible use of AI-assisted development tools
Human oversight, accountability, and responsibility within AI systems
Data-centric engineering and data governance approaches
Environmental and sustainability considerations associated with AI systems
Explainability, interpretability, and verifiability of AI models and systems
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 39:00 | 39:00 | Preparation and completion of summative report |
| Scheduled Learning And Teaching Activities | Lecture | 10 | 2:00 | 20:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Preparation and completion of formative problem-solving exercises |
| Scheduled Learning And Teaching Activities | Lecture | 1 | 1:00 | 1:00 | Revision session |
| Structured Guided Learning | Lecture materials | 20 | 1:00 | 20:00 | Reading through lecture materials – preparing / follow up |
| Guided Independent Study | Assessment preparation and completion | 1 | 50:00 | 50:00 | Preparation and completion of summative group report |
| Guided Independent Study | Directed research and reading | 20 | 1:00 | 20:00 | Wider reading as per provided reading list |
| Scheduled Learning And Teaching Activities | Practical | 10 | 2:00 | 20:00 | Supervised practical sessions focused on hands on implementation |
| Structured Guided Learning | Structured non-synchronous discussion | 10 | 1:00 | 10:00 | Q&A supporting lecture materials |
| Total | 200:00 |
Teaching Rationale And Relationship
This module includes an encounter with the leading edge of research, industry, practice and society
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 1 | M | 50 | Individual report, 2500 words covering theoretical concepts and practical skills covered in the module on AI Safety |
| Report | 1 | M | 50 | Group report, 3000 words on Responsible and Trustworthy AI covering social, legal, and ethical issues, as well as a reflective component |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Written exercise | 1 | M | Problem-solving exercises of 50 mins |
Assessment Rationale And Relationship
The individual report assesses students’ ability to evaluate and apply principles associated with safe, responsible, and trustworthy AI systems within practical and theoretical contexts. Students will analyse approaches to AI safety, risk assessment, system behaviour, and responsible design, demonstrating their ability to assess trade-offs and communicate informed technical and professional judgements. The assessment particularly supports the evaluation of students’ ability to analyse system behaviour and risks associated with AI deployment. (MLO2, MLO3)
The group report assesses students’ understanding of responsible and trustworthy AI from ethical, legal, societal, and professional perspectives. Students will collaboratively investigate and evaluate issues associated with the development and deployment of AI systems, considering concepts such as fairness, accountability, transparency, governance, and societal impact. The collaborative nature of the assessment supports peer learning and enables students to engage with diverse perspectives within a rapidly evolving field. (MLO1, MLO3, MLO4)
The formative written exercises provide opportunities for students to practise critical analysis, problem solving, and evaluation of responsible AI concepts throughout the module while receiving feedback to support ongoing development and reflection. (MLO1, MLO2, MLO3, MLO4)
The assessment structure supports the development of both technical and critical evaluative skills, enabling students to assess AI systems responsibly and communicate informed arguments relating to safe, ethical, and trustworthy AI practice within professional and real-world contexts.
Alternative assessment:
For students with a Student Support Plan (SSP) who have alternative assessment as a reasonable adjustment and/or students with supported Personal Extenuating Circumstances (PEC), it may be necessary to set an alternative assessment as presented below. Due to the professional requirements of some programmes, alternative assessments may not be available even where students are eligible.
Where appropriate, an individual coursework assessment based on the same themes and learning outcomes may be offered as an alternative to the group report, scaled appropriately for individual completion.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC3003's Timetable