11-830 Computational Ethics for NLP

Course Information

Time: Tue-Thu 10:30 am - 11:50 am
Room: 8427 WEH
Instructors: Yulia Tsvetkov
Alan W Black
Teaching Assistant: Shrimai Prabhumoye


As language technologies have become increasingly prevalent, there is a growing awareness that decisions we make about our data, methods, and tools are often tied up with their impact on people and societies. This course introduces students to real-world applications of language technologies and the potential ethical implications associated with their design.

The class will study advanced topics in Natural Language Processing, in or near the intersection of Machine Learning, Linguistics, and Computational Social Science. Centered around classical and state-of-the-art research, lectures will cover philosophical foundations of ethical research along with concrete case studies and ethical challenges in development of intelligent systems. Methodologically, we will discuss how to analyze large scale text generated by people to or about other people and how to reason about it through text-, network-, and people-centered models. From an engineering perspective, there will be an emphasis on practical design and implementation of useful and ethical AI systems, with annotation and coding assignments and a course project.

Topics include: misrepresentation and bias, including algorithms to identify biases in models and data and adversarial approaches to debiasing; privacy algorithms for demographic inference, personality profiling, and anonymization of demographic and personality traits; techniques to monitor civility in communication, and detecting trolling, hate speech, abusive language, cyberbullying, and toxic comments; propaganda and manipulation in news, and methods to identify fake news and political framing; healthcare and biomedical text processing and applications; low-resource NLP and its applications for disaster response and monitoring diseases in developing regions.


60% Homeworks (4 HWs of 15% each)
30% Project
10% Class Participation
Note: There will be no exam.

Tentative Schedule of Lectures

Date Topic Reading Homework
Week 1 Introduction
Motivation, requirements, overview
Week 2 Philosophical Foundations
Week 3 Misrepresentation and bias: stereotypes, prejudice, and discrimination
Week 4 Misrepresentation and bias: debiasing
Week 5 Project Proposal
Identification of trolling, hate speech
Week 6 Identification of abusive language, toxic comments
Modeling respect, power, agency in discourse
Week 7 Demographic inference techniques
Personality profiling
Week 8 Privacy and anonymization
Computational propaganda
Week 9 Computational propaganda
Framing, metaphor
Week 11 Biomedical NLP
Biomedical NLP
Week 12 Ethical Design
Annotation process and considerations
Week 13 Digital preservation
Intellectual Property
Week 14 Advance Topics
Advance Topics
Week 15 Project Presentations
Project Presentations



Late Homework Policy

Homework is due at the time given on the homework on the due date.

A penalty of 10% will be applied to homework that is submitted up to 24 hours late.

No credit will be given for homework that is submitted more than 24 hours after it is due.

Academic honesty

Homework assignments are to be completed individually. Verbal collaboration on homework assignments is acceptable, as well as re-implementation of relevant algorithms from research papers, but everything you turn in must be your own work, and you must note the names of anyone you collaborated with on each problem and cite resources that you used to learn about the problem. The project is to be completed by a team. You are encouraged to use existing NLP components in your project; you must acknowledge these appropriately in the documentation. Suspected violations of academic integrity rules will be handled in accordance with the CMU guidelines on collaboration and cheating: Carnegie Mellon University Policy on Cheating and Plagiarism.