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This course focuses on the technical challenges in data science that societal and regulatory actions pose. It aims to introduce students the often very different and sometimes even conflicting perspectives from which policymakers and the technical community approaches these problems. We will review and discuss different examples from different areas of data science such as the extent to which machine learning and deep learning techniques conform with GDPR regulations on transparency, explainability, and accountability; impossibility theorems showing off the limits of data science methods; the mathematical foundations and data science techniques for mechanism design in order to manipulate beliefs (represented as transitive, anti-symmetric, and complete binary relations); and provide students as potential future product developers with the necessary knowledge to engage in responsible product development practices that are informed by regulatory requirements and expectations. This course develops students' understanding of the role of data science in decision making and the impact of data science in the design of AI systems. The course reflects the main issues of controversy identified in international policy debates.
This course develops students’ understanding of the role of data science in decision making and the impact of data science in the design of AI systems. The course reflects the main issues of controversy identified in international policy debates.
On successful completion of this course, students will have: An overall understanding of data science principles in AI and various applications across different domains.An understanding of key theoretical foundations and concepts concerning data science research and development, as well as deployment of AI technologies. Developed a general awareness of data science techniques and international guidelines for designing new AI systems.Acquired the capacity to work independently and manage their time in order to meet course deadlines. Students will develop valuable writing and presentation skills.
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Critically competent in a core academic discipline of their award
Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
Biculturally competent and confident
Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree.
Globally aware
Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.
Subject to approval of the Head of Department of Mathematics and Statistics.
Students must attend one activity from each section.
Gabor Erdelyi
Domestic fee $1,023.00
International Postgraduate fees
* 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 .