Module Specification

Ethics of AI

Module Specification

Ethics of AI



Delve into the critical realm of 'Ethics of AI', a comprehensive module designed for participants seeking an advanced understanding of artificial intelligence within ethical frameworks. This innovative module provides a thorough introduction to AI and its ethical considerations, laying the groundwork for informed applications and AI development. Engage with the intricacies of data in AI systems, and scrutinize contemporary challenges arising from these rapidly evolving technologies. Explore how AI-risk analysis, alongside applications across sectors, demands a multifaceted understanding of potential impacts. Gain insights from various disciplines to form a well-rounded approach to AI ethics, understanding core principles and the importance of robust regulation and legislation. Equip yourself with strategic tools to manage AI risks effectively, and look ahead to the revolutionary prospects of Artificial General Intelligence and the complexities of machine copyright and moral agency. Enhance your strategic acumen by learning to craft and implement detailed AI ethics strategies, ensuring responsible integration of AI into your field. This module is pivotal for those aiming to lead in a future dominated by AI, ensuring a solid ethical foundation amidst technological advancements.



Prerequisites and Co-requisites

None

Learning Outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Comprehensive knowledge of AI's current ethical dilemmas, across diverse sectors, and awareness of new insights.
LO2 Intellectual Skills Analyse risk in AI systems, based on emerging legal frameworks, for the purposes of algorithmic accountability and decision-making efficacy.
LO3 Intellectual Skills Critique the application of AI ethics and assess the role of foundational principles in AI applications.
LO4 Intellectual Skills Systematically evaluate the relationship between data and AI, including privacy concerns and legal standards such as GDPR.
LO5 Technical/Practical Skills Propose governance systems and ethical codes to advance AI responsibly, considering global societal impact and sustainability.
LO6 Professional/Transferable Skills Communicate AI ethics strategies to diverse audiences for organisational and policy adaptation, informed by interdisciplinary insights.

Content Structure

Week Topic
Week 1 Introductory lecture
Introduces the module, outlining its relevance to the field and connections to other topics. It provides an overview of the content structure, key references, and assessment details.
Week 2 Data and AI Ethics
Focusing on data's crucial role in AI, this chapter examines how data is sourced, its relationship with AI, and the ethical challenges it presents. It encompasses the importance of data integrity, the consequences of breaches, and the legal frameworks like GDPR that govern data privacy.
Week 3 Contemporary Ethical Dilemmas
Students will engage with current ethical issues in AI such as generative AI's potential for misinformation, healthcare ethics, and the risks of deep fakes. The chapter encourages critical thinking, including when using AI and prompts, around ethical responsibility and compliance within the digital sphere.
Week 4 Sector-Specific Applications of AI
Focusing on how AI is applied across various sectors, this chapter examines the ethical considerations in weaponised AI, machine authorship, and corporate responsibility. It addresses the ethical toolkits available for responsibly integrating AI into multiple industries.
Week 5 Interdisciplinary Insights
An examination of the ethical frameworks applied to AI, informed by insights from philosophy, economics, law, and media studies. Students will understand the black box problem and roboethics, considering the broader impact of AI on society.
Week 6 Legal Frameworks
The chapter provides an in-depth exploration of risk analysis and the emerging legal instruments tailored to AI, including the EU’s AI Act. It deciphers the intersection of legal standards with AI ethics and profiles the risks associated with algorithmic decision-making.
Week 7 AI Governance
This chapter delves into governance systems that guide AI development, dissecting codes of ethics and comparing frameworks such as UNESCO's Ethical AI Framework. It explores how governance shapes AI for social good and holds stakeholders accountable.
Week 8 Crafting Strategies
Students will learn to create strategies for ethical AI, weighing considerations like corporate responsibility and sustainable development goals. The discussion includes value-sensitive design and the criticality of staff training in ethical technology deployment.
Week 9 Ethical AI in Practice
The chapter centres on putting ethical AI strategies into practice, discussing the frameworks and decision-making processes required to institute responsible AI deployment at both organisational and policy levels.
Week 10 Future Trends
Exploring the horizon of AI ethical considerations, this chapter discusses the prospect of Artificial General Intelligence and its ethical challenges, such as machine personhood and moral agency.
Week 11 Impact with AI Ethics
In this concluding chapter, we examine how ethical approaches in AI can shape technology policy and legislation, ensuring impactful advancements while maintaining accountability. It embraces the full scope of AI ethics, educating future leaders on responsible innovation.

Student Workload

The methods of teaching and learning for this module are based on the School's Short-course 5 teaching system, consisting of the following activities.

Activity
Introductory lecture
Concept learning (knowledge graph)
AI formative assessment
Case Study Review
Independent reading, exploration and practice
Total:

Teaching and Learning Methods

Activity Description
Introductory lecture

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.

Concept learning (knowledge graph)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.

AI formative assessment

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.

Case Study Review

In this learning activity, students explore recent real-world case studies relevant to their course topic. The case studies will have been selected and curated by the module leader to represent up-to-date examples. They guide students through key details, contextual factors, and outcomes. This approach enhances students' understanding of current industry trends, challenges, and solutions, preparing them for real-world scenarios they may encounter in their future careers.

The learning experienced will be augmented by AI (virtual private tutor) allowing the students to critically engage with the content and discuss the case studies.

Independent reading, exploration and practice

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.


Module Approval

Stage Version Date of approval Authority Chair Revalidation
Compliance 1.0 Academic Board Dr Paresh Kathrani
Pre-Teaching 1.0 Director of Education Dr Paresh Kathrani
Note: The information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Ethics of AI (AI02)