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This course provides students with a basic understanding of the concept of artificial intelligence (AI) and the existing spectrum of AI technologies. It has an easily understandable, lay-person-accessible format, requiring no prior mathematical or computer science knowledge. The course also gives an overview of the evolving AI legal, regulatory, and policy landscape. Upon successful completion of the course, students will possess the necessary technical understanding, research, analytical, problem solving, as well as collaboration and communication skills to tackle legal, regulatory, and policy issues related to the development and societal adoption of AI technologies independently or as member of an interdisciplinary team.
This course aims to equip students with the necessary skills to thrive in the digital age and under- stand the incredibly tough choices we—both as individuals and society as a whole—have to make with respect to the regulation, development, and societal adoption of AI technologies to ensure they are beneficial for humanity and our environment.As a data science student, are you interested in the regulatory and policy requirements that guide AI-related product engineering and development processes? Would you like to learn how to work with policy experts to translate abstract regulatory principles into readily implementable guidance informing the technical configuration of the products you will design and build?As a law or criminal justice student, would you like to learn about the promise and perils of engaging AI in the criminal justice system? Would you like to be a lawyer and know more about how you can leverage AI in your practice? Are you interested in working in the legal, compliance, or governance department of an AI developer, and want to get some experience in working with technical AI experts like researchers, product engineers, and developers? Or maybe you fancy a career in policymaking and wish to learn more about designing AI regulations and policies?As a student with a different background, would you like to know how AI affects your area? Or, equally importantly, how your expertise may benefit the AI field?Would you like to explore indigenous perspectives related to AI? Or would you like to learn more about what using AI in your everyday life really entails? This course answers those and many more questions, organized in two main thematic units.The first, technical unit introduces the concept of AI and the necessary technical foundations to un- derstand how AI technologies work. We do this in an easily understandable, lay-person-accessible format, requiring no prior mathematical or computer science knowledge. Familiarity with these foundations is the single most important prerequisite for making informed decisions related to AI—be it in the realm of regulation and policymaking, business, product development, and engi- neering decisions, or when it comes to determining our attitude towards using AI technologies in our everyday lives.The second unit focuses on key ethical, policy, and regulatory considerations related to AI and technological innovation more generally. It starts by reviewing the challenges associated with previous waves of technological innovations to establish the properties of a regulatory and policy stance that is conducive to the safe adoption of new technologies. We then examine current pol- icy and regulatory initiatives around responsible and trustworthy AI We also go into regulatory theory—and AI regulation and governance in particular—to familiarize ourselves with the multi- tude of considerations necessary for effective regulatory intervention in general (i.e., everything we need to consider before actually starting to design specific laws). This unit also includes (po- tentially science-fiction-based) case studies to spark thought-provoking, interactive discussions linked to real-world problems stemming from AI.Complementing these theoretical units, the course also covers some practical issues, e.g., real-life examples of how AI principles are implemented within an organization from setting top strategic priorities down to designing specific product features. Even the assessments have a strong practical touch. They are configured to prepare students for the challenges they will need to rise to in AI-related roles: International, interdisciplinary, and multi-stakeholder collaboration; team work; tricky communication issues, out-of-the-box, analytic thinking; constant need to research and solve novel, complex, and interdisciplinary problems, and more.The assessments and a potential guest lecture give you opportunity to delve deeper into selected, specific AI-related topics of your interest, which are beyond the core course content. This is essentially a flipped classroom scenario.Upon successful completion of this course, you will have acquired a unique blend of theoretical and practical training you will find useful in any AI-related role—or really any role. After all, AI is inescapably here to stay and raises challenges in virtually all legal and social domains!IMPORTANT: Students of all backgrounds are welcome! The more interdisciplinary the cohort, the more interesting and multifaceted problems we can tackle in the assessments. Just like in real life, many disciplines contribute to solving AI-related challenges and no single person knows everything. So, although the course touches on a lot of areas, you are not expected to know all of them. What matters is how we can work together as a team with everyone bringing their own insights to the table.All information is tentative and to be confirmed in the first week of lectures.
(1) Any 60 points at 200-level from Schedule C and S to the Bachelor of Data Science; or (2) LAWS101.
DATA416
For LLB students: LAWS202-206. For BDataSc and other non-LLB students: N/A.
LLAW305
Students must attend one activity from each section.
Olivia Erdelyi
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Domestic fee $946.00
International fee $4,850.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Mathematics and Statistics .