This module addresses topics of:

Introduction to Human-Centred AI:
Definitions and principles of human-centred AI and socio-technical systems.
Relationship between AI engineering, design, policy and society.

Ethical theories and AI:
Overview (at a non-technical level) of key ethical theories (consequentialist, deontological, virtue ethics) and their relevance to AI decision-making.
Limitations of purely rule-based vs purely outcome-based approaches in complex socio-technical systems.

Fairness, bias and discrimination in AI systems:
Sources of bias in data and models; representation harms and outcome harms (conceptual level, without overlapping with technical ML modules).
Protected characteristics, equality and non-discrimination; examples from hiring, credit, policing, health, and education.

Accountability, transparency and explainability:
Accountability frameworks: who is responsible for what, and to whom.
Types and purposes of explainability (Ex. user explanation, regulator explanation, developer tools).
Limits of explanation and trade-offs with performance, privacy and security.

Human oversight, control and autonomy:
Human-in-the-loop, human-on-the-loop and human-in-command models.
Designing AI systems that support, rather than replace, human judgement and expertise.
User control, consent and meaningful choice.

Legal and regulatory landscape for AI:
Overview of key regulatory instruments affecting AI (Ex. data protection, automated decision-making rules, sector-specific regulations, emerging AI regulation).
Roles of standards bodies, professional codes (Ex. BCS, IEEE) and organisational policies.
Global perspectives and governance challenges across different jurisdictions.

AI, security and societal risk:
AI-enabled threats and misuse (Ex. deepfakes, disinformation, targeted manipulation, security analytics misuse) from an ethical and governance perspective (distinct from technical cyber security modules).
Organisational and societal resilience, incident response and risk mitigation strategies.

Sustainability and justice in AI:
Environmental costs of AI (Ex. compute, energy, hardware) and trade-offs.
AI, labour and work: automation, augmentation and the future of jobs.
Justice, equity and global perspectives (Ex. AI’s impact on marginalised communities, Global South).

Human-centred AI design and co-creation:
Participatory and user-centred design approaches for AI systems.
Stakeholder mapping, journey mapping and scenario-building.
System modelling and evaluation of systems in context.

Professional practice and interdisciplinary collaboration:
Roles and responsibilities of AI practitioners in industry and public sector.
Working within multi-disciplinary teams (technical, legal, design, domain experts).
Communicating complex ethical and socio-technical issues to different audiences.

BCS / TechSkills / Employability elements:
Legal, social, ethical and professional issues (central theme).
System modelling and evaluation of systems in their socio-technical context.
Security and risk (AI misuse, disinformation, organisational controls) at conceptual level.
Management and planning of responsible AI deployments, including governance structures and accountability chains.
Sustainability (environmental and socio-economic dimensions).

PRACTICAL - Group Case Study (50%)
Working in small groups, you will be assigned (or choose from a set of curated options) a real-world AI application or scenario (Ex. automated hiring, predictive policing, medical triage, recommender systems, or generative AI tools). You will need to:

Map the socio-technical system and key stakeholders.
Identify key ethical, legal and societal issues using a structured framework (Ex. fairness, accountability, transparency, human oversight, accessibility, inclusivity, and sustainability).
Propose a set of concrete, realistic mitigations and governance mechanisms (Ex. data governance, transparency measures, escalation processes, or user safeguards).
Present analysis and recommendations in a professional 15-minute briefing to a mixed audience (imagined stakeholders), supported by a concise written set of briefing notes (policy/management style, not academic essay).

REPORT - Individual Critical Report (50%)
You will select an AI application area (which may be different from the group case) and produce an individual critical report that:

Describes AI systems and their context of use, including stakeholders, frameworks, power relations and relevant regulatory environment.
Reflect on your own role and responsibilities as a future AI professional, including tensions between commercial, technical and societal objectives.

All teaching sessions will blend conceptual input with applied, discussion-based learning. You will be introduced to core ideas and frameworks and then apply them to realistic scenarios and case studies. Learning activities may include:

Lectures to introduce key concepts, frameworks and exemplars in human-centred AI and ethics.
Seminars to discuss readings, current events and case studies in depth.
Practical classes and workshops focused on stakeholder mapping, impact assessment, and scenario-building.
Tutorials / drop-in sessions for guidance on assessment topics and feedback on emerging ideas.
Groupwork and role-play/simulation activities to rehearse stakeholder negotiations and deliberations around AI deployments.

You will be provided with curated independent study materials, such as policy documents, professional guidelines, academic papers, industry reports and multimedia resources, to deepen your understanding and support assessment preparation. Formative (non-graded) exercises will help you to practice applying frameworks and receiving feedback before undertaking formal assessment.

1. Explain key concepts, frameworks and debates in human-centred artificial intelligence.

Knowledge & Understanding
Research Skills

2. Critically analyse real-world AI applications using structured ethical and socio-technical frameworks.

Application & Problem-Solving
Critical Reasoning & Collaboration

3. Propose human-centred design and governance strategies for AI systems.

Communication
Personal Development & Entrepreneurship

4. Communicate evidence-based recommendations on the responsible design and deployment of AI systems to both technical and non-technical audiences.

Reflection
Critical Reasoning & Collaboration

Students will require access to:

University VLE (Blackboard) for learning materials, announcements and submission of assessments.
Library access (physical and digital) for academic literature, policy and regulatory documents.
Standard productivity tools (Ex. word processing, presentation software, online collaboration platforms).
Case-study packs and scenario materials provided by the teaching team.

No specialist software or laboratory facilities are required beyond typical Computing lab and seminar facilities, although access to news, policy and technical-report databases is strongly encouraged.

Sorte, A. (2025) AI & Ethics: Towards a Safe and Fair Future in the Age of Artificial Intelligence, Independently Published.

Floridi, L. and Cowls, J. (2022) Ethics, Governance, and Policies in Artificial Intelligence, Oxford University Press.

Gupta, K. (2025) 2034: How AI Changed Humanity Forever, Independently Published.

Ada Lovelace Institute (2021) Examining the Black Box: Tools for Assessing Algorithmic Systems [Online] Available at: https://www.adalovelaceinstitute.org

IEEE (2019) Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, IEEE Standards Association.

In this module, you will explore how artificial intelligence systems affect people, organisations and society, and how they can be designed and governed responsibly. You will examine real-world AI applications through the lenses of fairness, accountability, transparency and human-centred design, and learn how emerging legal and regulatory frameworks shape what is acceptable in practice. Through case studies, scenario-based activities and a substantial individual project, you will develop the skills to analyse ethical and societal risks, propose practical governance and design strategies, and communicate your recommendations clearly to both technical and non-technical stakeholders.