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You can do a degree in Artificial Intelligence at Leiden University, but its role is also increasing in other degree programmes.

Who will be the winner in the digital world?

Digital Anthropology

Cultural Anthropology and Development Sociology (BA)

Digitalisation has drastically changed our lives in a relatively short time. What are the social implications of digitalisation in the different societies in the world?Cultural Anthropology and Development Sociology (BSc) students research this question as part of the Digital Anthropology course. As often, the main implications seem to relate to power: who are the winners and losers in the digital world? How are traditional power structures responding to the rise of digital networks and how does this differ by society? And what effects are digital technologies having on hierarchies within society such as class, race and gender? By delving into the existing literature and applying scientific concepts in practical assignments, students learn how to gain insight into these important developments.  

Big data and the public domain

Data-Driven Policymaking

Public Administration (MA) & ICT in Business and the Public Sector (MA)

Big data is watching you. Besides commercial companies, governments too are increasingly basing their decisions on digital datasets, or big data as they are known. In the Data-Driven Policymaking course, students look at how big data is used in the public domain, and consider the pros and cons of this development. They also consider whether governments have the resources and expertise needed to handle these new information flows and the extent to which big data can help them foster public participation. We are now in an era when policies on such issues as infrastructure or health care are largely based on datasets. The question is whether we can actually have confidence in these data-driven decisions?

How deep learning supports linguistics

Introduction to Deep Learning and Natural Language Processing

Linguistics (BA)

In the Introduction to Deep Learning and Natural Language Processing course on the Linguistics (BA) programme, linguistics students explore the phenomenon of deep learning and how it is used to analyse language. Deep learning is a technique for processing information where computers independently learn to analyse unstructured datasets and thus search for patterns. This is a very useful method for analysing data with a lot of natural nuances and variation, such as language. As part of this course, students learn the basics of deep learning methods and how to use these techniques themselves by programming algorithms in practical sessions.

Can we entrust everything to an AI system?

Law and Artificial Intelligence

Elective course, Leiden Law School

The growth of AI raises unique new questions in the present legal and regulatory framework. In the Law and Artificial Intelligence elective course, students make a critical study of the latest developments in AI and their legal implications. As AI systems – facial recognition software, robot or decision support systems, for instance – operate largely independently, this raises particular legal issues. Can we really entrust everything to a digital system? How transparent and secure are AI systems? And how great is the danger that these algorithms may exacerbate existing prejudices about population groups, thus encouraging discrimination? In this course students analyse the challenges that AI presents for legislation and regulations, and explore possible solutions.

Computer-driven research methods

Hacking the Humanities: An Introduction to Python and Text Mining

Digital Humanities (minor)

AI offers researchers and the scientific world previously unimagined possibilities. Its algorithms have made it easier than ever before to structure and analyse texts and academic sources. The Hacking the Humanities elective course introduces humanities students to these computer-driven research methods, acquaints them with the current debates within the digital humanities and teaches them the finer points of the Python programming language. How can academics use this tool to analyse historical or linguistic texts? Whether or not students have previous experience with programming, they will, by the end of the course, be able to write a program that automatically extracts, analyses and visualises information from academic sources. 

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