Machine Learning and Natural Language technologies are integrated in more and more aspects of our life. Therefore, the decisions we make about our methods and data are closely tied up with their impact on our world and society. In this course, we present real-world, state-of-the-art applications of natural language processing and their associated ethical questions and consequences. We also discuss philosophical foundations of ethics in research.

Core topics of this course:

- Philosophical foundations: what is ethics, history, medical and psychological experiments, ethical decision making.
- Misrepresentation and bias: 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 personal traits.
- Civility in communication: techniques to monitor trolling, hate speech, abusive language, cyberbullying, toxic comments.
- Democracy and the language of manipulation: approaches to identify propaganda and manipulation in news, to identify fake news, political framing.
- NLP for Social Good: Low-resource NLP, applications for disaster response and monitoring diseases, medical applications, psychological counseling, interfaces for accessibility.