Course details
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Aims
This course aims to:
• introduce you to the essence and the social impact of Big Data and AI
• provide you with knowledge and understanding of the fundamental ethics principles that underpin the governance of Big Data and AI
• enable you to reason about and reconcile tensions among risks and benefits of Big Data and AI through the application of these fundamental ethics principles
Course content
This course provides an introduction to the main ethical implications of Big Data and AI, and to the fundamental ethics principles for their governance. The widespread use of Big Data and AI has surfaced a range of opportunities and challenges. Regulations are unable to fully address and balance the associated benefits and risks. This course explores the role of ethics in this endeavour. We first examine the essence, benefits and limitations of Big Data and AI, and their ethical implications. You are then introduced to the crux of ethics and its role in addressing the ethical implications of Big Data and AI. A separate session provides an initial overview of the fundamental ethics principles – i.e. of human autonomy, non-maleficence, beneficence, and justice – and briefly discusses the utility and limitations of ethics principles as a whole. In five separate sessions, you come to understand the meaning of each fundamental ethics principle in more detail. We will uncover the core of each principle and the normative guidance that it provides in the areas of Big Data and AI. By drawing on specific case studies, we then explore the application of the principle in practice and the associated tensions. In the ninth session of the course, we discuss practical approaches to resolving these tensions through the methods of ethics. We will conclude with a practice-orientated session on incorporating ethics into the governance of Big Data and AI at various organisations.
What to expect on this course
You will delve into the ethics of Big Data and AI through interactive ways of teaching. Building upon real-life examples, we will explore fundamental theoretical concepts and their practical application. Throughout each session, you will be invited to hone your critical thinking and problem-solving skills by engaging with case studies, specific questions and other tasks that would challenge your moral intuitions and perspectives. You will also have the opportunity to ask questions in each of the sessions.
Course sessions
1. Big Data and AI: essence, opportunities, challenges and implications
We briefly review the course programme, objectives, and outcomes. You then get acquainted with the key features, limitations and applications of Big Data and AI. We also examine the main opportunities, challenges and implications that Big Data, AI and their use give rise to.
2. Ethics: crux and role in addressing the implications of Big Data and AI
This session introduces you to the essence and objectives of ethics, and its significance in the governance of Big Data and AI. We discuss the distinctions between right and wrong, and good and bad, and what moral judgements purport to achieve. A high-level and accessible overview of the main normative ethics theories follows, in order to illustrate the role and application of ethics to Big Data and AI.
3. Fundamental ethics principles: an overview
Building upon the key concepts introduced in the previous session, this lecture provides an initial overview of the fundamental ethics principles of human autonomy, non-maleficence, beneficence, and justice. We then briefly explore the utility and limitations of ethics principles as a whole. We conclude the session with observations about the advantages and constraints of using ethics as a governance tool.
4. Privacy
This session first examines the essence of privacy and the right to privacy, and the difference between the two concepts. By drawing on the various connotations of privacy, we elicit its meaning, as well as resulting objections and moral tensions in the context of Big Data and AI. You come to understand how privacy relates to autonomy, which we discuss in the next session. We examine practical approaches to applying the principle of privacy to Big Data and AI, and to navigating moral tensions. A case study from the healthcare domain helps us illustrate all these aspects.
5. The principle of human autonomy
We unravel the meaning of this principle by examining its prevalent conception, the main objections thereto and resulting moral tensions. The session further delves into the application of the principle in the areas of Big Data and AI. We also consider possible approaches to addressing the more prominent objections to the principle and resulting moral tensions. We draw on specific examples from behavioural advertising and healthcare to illustrate the various points.
6. The principle of non-maleficence
Harm prevention is the lead consideration driving most efforts for governing Big Data and AI. This consideration emanates from the principle of non-maleficence or “do no harm”. We explore its essence, requirements, limitations, main objections and attendant moral tensions. The practical application of the principle to Big Data and AI is then discussed. Approaches to navigating objections and moral tensions are mapped out. Case studies from the domains of biosecurity and social media illustrate and clarify all these aspects.
7. The principle of beneficence
This session explores the meaning of the principle, its main limitations and the moral tensions that it gives rise to. We discuss the implementation of the principle in the context of data and AI, including how to address objections and tensions. Examples from childcare and environmental protection illustrate the meaning and application of the principle.
8. The principle of justice
The session reviews the main interpretations of the principle, their strengths and weaknesses, and resulting moral tensions. We next discuss the interpretation of the principle in the context of Big Data and AI, including through the various fairness and non-discrimination metrics and requirements for transparency and accountability. We then examine practical approaches to pursuing justice in Big Data and AI while tackling associated tensions. Case studies from healthcare and recruitment ground these discussions.
9. Applying principles in practice
The fundamental ethics principles may at times appear at odds with each other and give rise to moral tensions. The successful implementations of the principles therefore require (some) reconciliation of these tensions. In this session, we explore the established methods of ethics for effective moral decision-making, such as deliberations, participatory co-creation, and value-sensitive design. Building upon the key takeaways and examples from the previous sessions, we discuss practical approaches to navigating moral tensions and disagreements.
10. Incorporating ethics in the governance of Big Data and AI
In this final session of the course, we explore a framework for implementing ethics-based governance of Big Data and AI. We discuss how ethics can ground and inform organisations’ mission, long-term objectives and nearer-term goals, corporate structures, policies, procedures and technical and organisational measures to manage the risks associated with Big Data and AI and benefit from their use.
Learning outcomes
As a result of the course, you will gain a greater understanding of the subject and you should be able to:
• demonstrate your grasp of the essence and the ethical and social implications of Big Data and AI
• understand and be able to explain the core and significance of the fundamental ethics principles governing Big Data and AI
• be able to identify, reason about and reconcile tensions among risks and benefits of Big Data and AI through the application of these fundamental ethics principles
Required reading
The following publications and textbook are particularly helpful and thus required readings in preparation for the course.
High-level Expert Group on AI, (2019, April 8) Ethics guidelines for trustworthy AI
OECD (2019, May 22), OECD Recommendation of the Council on Artificial Intelligence. Section 1: Principles for responsible stewardship of trustworthy AI
UNESCO (2021, November 23), Recommendation on the Ethics of Artificial Intelligence, Chapter III: Values and principles