Admission requirements
It is strongly recommended that students:
have proficiency in Python
have a basic knowlege in Machine Learning
are interested in Natural Language Processing and its connection with society.
It is also recommended that students have familiarity with Generative AI systems like GPT4.
Description
Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables computers to interpret and generate human language. This field has become a cornerstone technology of the information age, as many of our social interactions are recorded as textual data—for example, social network posts, medical records, or customer evaluations. During the 2010s, deep learning techniques emerged and led to remarkable improvements in NLP tasks. A few years later, in the 2020s, the emergence of large language models (LLMs) like the ChatGPT core marked a major turning point in the field.
This course examines the potential of NLP research to provide societal benefits. Students will learn and apply the latest NLP techniques to address societal issues including hate speech, sentiment and emotion analysis, misinformation, and social movements. Ethical considerations are integrated throughout the course. Students will engage with topics including algorithmic bias and fairness, privacy and surveillance, data representativeness and annotation practices, and the environmental costs associated with training LLMs.
Students will be encouraged to think critically about both the opportunities and limitations of NLP technologies in real-world contexts. Through lectures, assignments, and a final project, they will learn to implement NLP techniques properly and evaluate their societal implications.
Course objectives
This course will provide an overview of the field of Natural Language Processing for Social Good. After the successful completion of this course, you will be able to:
Understand the Foundations and Evolution of NLP.
Understand and identify NLP for good challenges, limitations, and potential.
Apply NLP methodologies to address real-world problems related to society.
Apply the latest research on NLP including large language models.
Critically analyze the ethical and human implications of NLP in society including algorithmic bias, privacy, data representativeness and the environmental impacts..
Critically engage in discussions on social-good topics, analyzing real-world challenges, ethical implications, and potential solutions inspired by guest presentations.
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Lectures, background reading, group discussion, preparatory exercises, assignments.
Assessment method
Grading is based on:
Assignments (3 total): 50%
Final project & presentation: 50%
To pass the course, students must:
Complete all assignments and the final project. Failure to submit any assignment or the final project will result in a final course grade of 0
Score at least 5.5 (out of 10) on both the average of all assignments and the final project individually. Only if both minimum scores are met will the final grade be calculated as a weighted average (50% assignments, 50% final project). The final weighted grade must be at least 5.5 (rounded to one decimal) to pass. If one or both of the minimum scores are not met, the student fails the course.
Completed assignments and projects are valid for one year. If a student fails the course, all components must be completed again the following year.
The deadline for each assignment and final project is binding; missing the deadline will automatically lead to a grade of 0.
Students who fail the final project can re-do it within a specified time frame. Students who fail the second attempt will not have a third chance for re-take. No retakes are offered for Assignments.
Reading list
(2016 ACL) The Social Impact of Natural Language Processing. Dirk Hovy, Shannon L. Spruit
(2021 ACL Findings) How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact. Zhijing Jin, Geeticka Chauhan, Brian Tse, Mrinmaya Sachan, Rada Mihalcea.
(2020 ACL) Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?. Kobi Leins, Jey Han Lau, Timothy Baldwin.
Study materials will be provided by the lecturer during the course.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Contact the lecturer (f.m.plaza.del.arco@liacs.leidenuniv.nl) for course specific questions and the programme coordinator for questions regarding the programme, admission and/or registration.
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.