top of page
Library Shelves

Programme Information

Through a rigorous curriculum that combines theoretical and practical approaches, students will gain a deep understanding of the ethical, social, political, and economic implications of AI. They will also learn how to apply this knowledge in various contexts, including policy-making, business strategy, ethical design, and creative industries.

The MA in AI, Ethics and Society has an interdisciplinary approach, drawing on a range of fields, including philosophy, computer science, social sciences, law, political science, and humanities.

The programme will equip graduates and professionals with the knowledge and skills to navigate the AI revolution and have a positive impact in their respective fields.

As part of the programme, students will have the opportunity to engage with cutting-edge research and innovation through HKU's AI&Humanity Lab, where they can access leading scholars and engage in discussions with visitors from around the world. Regular talks and presentations from experts in the field will also provide a platform for students to expand their knowledge and explore the intersection of artificial intelligence and society.

Programme Requirement

 

The 60 credit programme consists of two semesters, distributed as follows:

3 required core courses (27 credits) (Semester 1)

3 elective MA courses (18 credits) (Semester 2)

1 MA portfolio project (15 credits) (Semester 2)

 

All instruction is in English and assessment is 100% coursework, which may include discussion, participation, oral presentations, tests, research essays, problem sets, group work, written reports, design projects, community outreach projects, industry outreach or internship projects, and other experiential learning activities.


Core Course

 

(Not all elective courses listed below will be offered each year)


PHIL7001  Fundamentals of AI, Data and Algorithms (9 credits) 
 
This core course presents the technical fundamentals of artificial intelligence and big data technologies, and how these are applied across a range of domains and sectors, such as medicine, business and government. It provides a thorough understanding of the current capabilities and status of these technologies. Focus will be on fundamentals which are relevant to understanding the philosophical import, and ethical, social and political implications of AI and big data. The course will cover the basics of a range of topics, which may include: large language models, neural networks, deep learning, supervised vs. unsupervised learning, reinforcement learning, knowledge-based agents, natural language processing, Bayesian learning, data analysis, statistical inference, decision theory, game theory, amongst other topics. The core competencies targeted by this course are a conceptual understanding of the way modern artificial intelligence systems operate, and a basic understanding of the tools used for understanding their import.

 

PHIL7002  Ethics: AI, Data and Algorithms (9 credits) 

This core course presents the broad ethical implications of artificial intelligence, the use of big data, and the role of algorithms in decision making. Students will be exposed to theories and toolsets for thinking about the normative implications of predicting and mitigating the ethical risks posed by the use of AI, data, and algorithms, including issues of fairness, procedural justice, and the like. Special focus will be on the social, moral, and economic effects of the widespread deployment of AI systems (e.g., the large language models that back-end user interfaces such as ChatGPT). At the end of the course, students will be able to explain the ethical complexities associated with different forms of modern artificial intelligence and deep learning techniques, ethical and privacy concerns related to the use of large data sets, the risks of adversarial attacks on otherwise harmless systems, and the potential risks and abuses of using algorithmic decision making in a range of social, political, and interpersonal contexts.

 

PHIL7003  The Nature of AI (9 credits) 

This course aims to provide students with a robust understanding of the history, nature, and likely trajectory of modern artificial intelligence, including how artificial intelligence has played a role in the development of new technologies. Particular topics will include the nature of artificial and human intelligence, comparisons between the capacities of machines and humans, and the potential capacities of future emergent technologies and machine capabilities. Questions include: are artificial systems meaningfully different from non-artificial systems? Can language-based AI systems be said to understand language? Is it possible for an artificial intelligence to suffer? How likely is artificial general intelligence – is it even possible? Should we worry about the possibility of an AI singularity, or are such risks either too opaque or too distant to be worth thinking about? AI has developed in relationship to a number of academic disciplines and technologies. This course will focus on these and other questions about the nature of AI from an applied philosophical rather than, e.g., an engineering or technical, perspective.
Elective Courses (Not all elective courses listed below will be offered each year)

 

Elective Course

PHIL7004  AI Safety and Security (6 credits) 

This course aims to provide students with an overview of current issues in AI safety and security. Questions include: How can we ensure that AI is interpretable? That is, how can we ensure that the behaviour and choices of sufficiently sophisticated AI systems are rationally transparent – able to be understood as supported by reasons – by human agents? How can we align AI with human values, objectives, desires, goals, and aims so that potentially quite powerful AI systems will not behave in objectionable ways? How can we ensure control of (potentially power-seeking) AI? How can we ensure that potentially dispersed AI systems are subject to human oversight and control? By the end of this course, students will be able to articulate the major safety and security challenges facing modern AI system design and the various extant approaches designed to solve these challenges. No previous background in machine learning or computer science is expected in this course.

PHIL 7005 AI Regulation and Governance (6 credits) 

The prevalence of AI and algorithmic decision making raises a host of governance issues and questions, including: How should privacy be protected in the use of large data sets? Are artificial agents subject to the same laws as humans? How can software be effectively regulated? Who is responsible for the potential lawbreaking behaviour of AI systems: (i) their designers, (ii) the individuals who own the hardware on which the AI is running at the time, (iii) someone else? How should AI be expected to behave when it is programmed to perform an action that is illegal? Should AI have a way to weigh illegal actions against one another? Must the capabilities of AI be published in the public domain? How do several and joint liability work in cases where different AI contribute to a single legally actionable outcome? Are there distinctive regulatory challenges faced by the introduction of AI systems? In this course, students will be exposed to a variety of theoretical frameworks designed to think carefully about these issues, and by the end of the course they will be expected to be able to analyse these and other regulatory and governance questions that arise in a variety of fields, including business, law, finance, criminal justice, etc. While focus will be on the identification and analysis of such issues, students will be exposed to examples of existing regulatory and governance frameworks as models and in order to engage critically with them.

PHIL7006  Minds and Machines (6 credits) 

This course compares the nature of the human mind to the minds, or proxies thereof, of complex machines. Students will explore theories of the nature of the mind and mental phenomena, including consciousness and mental representation, the relationship between the mind and the brain, and the relationship between the mind and external tools (e.g., smartphones) we exploit to extend the capacities of our minds. After establishing a firm foundation in these topics, the course will cover the theoretical foundations of research programmes in computational cognitive science and artificial intelligence research, in order to address what these philosophical and scientific theories tell us about the nature and capacities of (potential) minds, or proxies thereof, of complex machines. The course may also explore ethical issues such as the normative aspects of mental representation, manipulation by machines, the extended mind, mind uploading, and the moral status of robots.

PHIL7007  Philosophy and Ethics of Virtual Reality (6 credits)  

This course provides an introduction to the current and foreseeable capabilities of virtual reality technology, the philosophy and ethics of virtual reality and more generally to technophilosophy, and to the social and political implications of virtual reality technology. Central questions include: What is augmented reality? What is a virtual reality and how is it related to augmented reality? How can we know that we’re not living in a simulated reality? Are virtual objects real and if so, in what sense? Can we live a good life in a simulated reality? What is the connection between mind and body in virtual reality? What do words mean in virtual reality? Are there special social, political, economic, moral and legal issues associated with (wide uptake of) virtual reality, or within virtual reality itself? What are the implications of VR on social, political, and economic organisation? How could and should such an organisation manifest within virtual reality itself? What principles of design and design challenges arise for those creating virtual reality technologies? By the end of this course, students will be able to articulate the major ethical, philosophical and practical issues and challenges posed by virtual reality technology, and the existing approaches to addressing these.

PHIL7008  Philosophy and Ethics of Information (6 credits) 

In this course, students will explore topics and issues in the philosophy and ethics of information. Information and communication technologies have transformed diverse aspects of our lives, including the nature of entertainment, work, privacy, social relationships, communication, elections, and warfare, to name just a few. The course will address the question of how information and communication technology has fundamentally changed the nature of and our concepts of work, privacy, communication, etc. The course will also explore the important and distinctive ethical challenges that arise with the advent of information and communication technologies, such as online pornography, the digital divide, free speech and censorship, mis- and dis-information, and fake news. The social and political epistemology of information will also be covered by exploring how it relates to search engines and the digital public sphere. In addition to explicitly normative issues such as those listed, the course will cover foundational topics and issues in information theory, including: the nature of information, the dynamics of information, information networks, the basic principles of information, applications of information theory, and measures and applications of the quality of information. Students completing this course will be able to articulate and analyse both practical and theoretical issues concerning information.

PHIL7009  Technology and Human Values (6 credits) 

This course will address questions pertinent to the more general topic of the philosophy of technology, value-sensitive design and critical design theory: What is technology? What is the relationship between technology and humanity? What are the appropriate methods and metrics for evaluating technologies and their role in society? How does disruptive technology affect our values, beliefs, concepts and social norms? How and when should humanity innovate? What is responsible innovation? What values should designers of technology possess in creating technology? Who is responsible for the harms of technology? How is technology regulated, and how should it be regulated? Can technology govern? Case studies will be of a more general nature and may include, but are not limited to: genetic selection, enhancement and eugenics, sex robots, chatbots and virtual assistants, automated weaponry, wearable or implantable technology, facial recognition, driverless vehicles, and digital or smart cities.

PHIL7010  Formal Methods for AI, Ethics and Society (6 credits) 

The course will allow students to build on their understanding of the technical fundamentals learnt in the core course Fundamentals of AI, Data and Algorithms. In addition to the topics covered in Fundamentals of AI, Data and Algorithms, topics may be chosen from among a selection of theoretically fundamental issues in AI, Ethics and Society, with an emphasis on the cross-disciplinary analysis of these issues. Like Fundamentals of AI, Data and Algorithms, the core competency targeted is a conceptual understanding of the way modern artificial intelligence systems operate, and on developing tools for understanding their import.

PHIL7011  AI, Ethics and Society Seminar (6 credits) 

The course will consist of both seminars and special learning activities. The latter might include tutorials and workshops, coding or design projects, field trips, company visits, community outreach, or other forms of experiential learning. Multiple forms of assessment will be used. The total output of written assignments should not exceed 8,000 words.

PHIL7012  AI, Ethics and Society Workshop (6 credits) 

In this course students will attend an academic or professional workshop whose topic is relevant to AI, Ethics, and Society. Preparation for the workshop will include (i) reading the relevant research to be discussed at the workshop, (ii) discussion of the material in advance of the workshop to prepare for the discussion (including collaborating with peers to develop questions and issues to address with the other participants of the workshop). At the workshop students will take notes and participate in a discussion of the workshop presentations. After the workshop students will prepare research reports on the issues discussed at the workshop, including outlines of plans for future work on the topics. Students enrolled in this course will be supervised by the seminar teacher throughout their preparation, attendance, and after-workshop activities. Seminar sessions will be conducted by the seminar teacher to facilitate planning, student coordination and sharing, peer-feedback, and joint discussion of relevant research, experiences, and culminating reports.

PHIL7013  AI in Business and Economics (6 credits) 

This course focuses on the applications of artificial intelligence (AI) in business and economics. Students will learn how various AI techniques can be applied to solve real-world problems in business and economics, such as market analysis, customer relationship management, human resources management, robo-advisors, algorithmic trading, risk management, and economic predictions. Case studies and the ethical challenges raised by the use of AI in business and economics, such as algorithmic bias, data bias, security risks, privacy violations, and lack of transparency, will be discussed.

 

 

 AI and Entrepreneurship (6 credits) Subject to Approval

This hands-on, project-based course provides graduate students with the real-world experience of leveraging AI technologies to ideate, build, and execute on an entrepreneurial venture. Through collaborative group projects, students will apply AI techniques to identify and validate innovative business opportunities. The course rapidly progresses from conceptualizing AI-driven ideas to formulating AI-powered business venture.

Students will gain insight into launching startups, from assembling teams to acquiring financing. Additionally, students will develop an understanding of the legal frameworks around entrepreneurship and intellectual property to inform how to protect and commercialize AI innovations. Upon completion, students will possess the practical abilities to advance an AI concept from ideation to real-world impact.

 AI in Business and Economics (6 credits) Subject to Approval

This experiential, project-based course provides graduate students with the real-world experience of conducting co-designed legal, regulatory and policy research with industry and non-profit organization partners. This course will allow students to apply the learnings from the core courses on understanding the philosophical import, and ethical, social and political implications of AI and big data. Students will be expected to work in teams in conjunction with their project partners throughout the semester on a publishable quality written and/or visual deliverable as well as at an end-of-semester presentation attended by their project partners who will provide feedback. Students will also be introduced to GenAI tools to be used for their projects.


 

Portfolio Project

PHIL7999 Capstone Experience: MA Portfolio Project in AI, Ethics and Society (15 credits) 

Students in this course will produce a portfolio of written work. This written work can take one of two basic forms: (i) academic writing targeted at and appropriate for engaged though not necessarily expert academic audiences and (ii) non-academic but nevertheless rigorously researched and carefully argued writing targeted at and appropriate for policy-makers, decision-makers, and other stakeholders. Examples of (i) include academic article-length papers addressed to a particular issue in AI, Ethics, and Society. Examples of (ii) include reports and policy papers (including executive summaries) addressed to a practical question touching AI, Ethics, and Society. These approaches are not mutually exclusive, and students are encouraged to diversify their portfolio throughout its development. Whichever combination of approaches is taken, students’ work will be based on assignments and research conducted during their course work, which will then be elaborated through independent research, peer review, and expert supervision. Students will apply the advanced methods, skills, and knowledge they’ve acquired throughout the programme to improve their portfolio projects and bring them to the standards of either academic or professional writing, sourcing, and presentation. In addition to supervision meetings, students will be required to attend a pro-seminar where they’ll present their work-in-progress and receive peer-feedback on their portfolio throughout its development.

bottom of page