Ethics in Natural Language Processing (WiSe 21/22)

General Information

Natural Language Processing (NLP) applications have become ubiquitous in our everyday lives: We translate texts with deepL or Google translate, ask the speech assistants in our home to play music or add item to our grocery list, and maybe turn on the automatically generated captions when watching a video on YouTube. This has started an ongoing discussion in the NLP community on the responsibilities and ethical concerns of researchers and practitioners alike when it comes to the design and use of NLP applications.

This course introduces students to the core concepts that play a role in the current discussion on Ethics in NLP (such as bias, privacy, dual use) and discusses how these can be concretely detected and addressed in various NLP applications (e.g., bias detection ad de-biasing of word embeddings). At the end of the course, students will be able to critically evaluate NLP resources, applications, and research and will be familiar with state-of-the art methods that have been proposed to address common issues in NLP.


The current syllable of the course. Please note that some of the contents might still be shifted or updated throughout the semester.

(last update 31.01.22)

Date Topic Presenters & chairs Pre-class reading Comments
25.10. Organisation & Foundations I Choose your presentation topic and chair session
01.11 no class (national holiday)
08.11. Foundations II Read Hovy & Spruit (2016), Leidner & Plachouras (2017), and Leins et al. (2020) Deadline (noon): participation mode, presentation topic & chair session
15.11. Privacy Presentation 1: Nkonye Gbadegoye, Fidan Can; presentation 2: Matthias Drews, Daniel Lehmann; chairs: Rofaïda Rabehi; Nicolai Plenk Presentation articles: Boyd & Marwick (2011), Jurgens et al. (2017)/Keküllüoglu et al. (2020); discussion material: read the news article assigned to your group on Moodle considering the discussion questions. Group 1, Group 2
22.11. Bias in sentiment analyses Presentation 1: Leixin Zhang; presentation 2: Soh-Eun Shim; chairs: Anna-Katharina Dick, Qin Gu Presentation articles: Bhaskaran & Bhallamudi (2019), Kiritchenko & Mohammad (2018); discussion material: read the blog entry assigned to your group on Moodle considering the discussion questions. Group 1, Group 2 Note: no in-person meetings from now on, Zoom only!
29.11. Bias & fairness Presentation 1: Leander Girrbach; chairs 1: Miriam Segiet, Mina Mottahedin; chairs 2: Matthias Drews Presentation articles: Ethayarajh (2020) and Ethayarajh’s blog article (more accessible); out-of-class input: watch Kate Crawford: The Trouble with Bias Shorter session!
06.12. Dual use & responsibility Presentation 1: Nino Meisinger, Qin Gu; presentation 2: Zarah Weiss; chairs 1: Daria Schmidt, Nkonye Gbadegoye; chairs 2: Benjamin Starzec, Joel Bondy Presentation articles: Johannßen et al. (2020), Floridi (2016)/Matthias (2004); discussion material: read Bender’s blog article and consider the following discussion questions. Note: from now on, the discussion material serves as mandatory background reading for the post-presentation discussions
13.12. Green NLP Presentation 1: Nicolai Plenk, Connor Kirberger; presentation 2: Ilinca Vandici; chairs: Marija Majstorovic, Hoa Do Presentation articles: Strubell et al (2019); Ethayarajh & Jurafsky (2020) background material: read Schwartz et al. (2019).
20.12. Research ethics Presentation 1: Marija Majstorovic, Belinda Deskaj presentation 2: Anna-Katharina Dick, Markus Schoch; chairs: Tatiana Merzhevich, Uliana Vedenina Presentation articles: Geiger et al. (2020), Ayres et al. (2018)/Fiesler & Proferes (2018); background reading: read Lipton & Steinhardt (2019)
27.12. no class (christmas break)
03.01. no class (christmas break)
10.01. Conversational agents Presentation 1: Daria Schmidt, Leyre Sánchez Viñuela; presentation 2: Ankur Saxena, Gurpreet Singh; chairs 1: M. Mourhaf Kazzaz, Jinghua Xu; chairs 2: Fidan Can Presentation articles: Chin et al. (2020)/Curry & Rieser (2018), Feine et al (2019); background reading: Think piece 2 in West et al. (2019)
17.01. NLP and health Presentation 1: Tatiana Merzhevich, Uliana Vedenina; presentation 2: Hoa Do, Eric Rebstock; chairs 1: Joana Burger/Leander Girrbach; chairs 2: Daniel Lehmann, Connor Kirberger Presentation articles: Gkotsis et al. (2016); Cohan et al. (2018); background reading: Benton et al. (2017), Šuster et al. (2017)
24.01. Bias detection Presentation 1: Miriam Segiet, Apoorva Rao Balevalachilu; presentation 2: Diana-Constantina Höfels, Mina Mottahedin ; chairs 1: Soh-Eun Shim, Leixin Zhang; chairs 2: Eric Rebstock Presentation articles: Ferrer et al. (2021), Voigt et al. (2018)/Chang & McKeow (2019); background reading: read Haslam (2006)
31.01. NLP, propaganda and misinformation Presentation 1: Rofaïda Rabehi, Joana Burger; presentation 2: Ben Starzec, Joel Bondy; chairs 1: Leyre Sánchez Viñuela; chairs 2: Gurpreet Singh, Ankur Saxena Presentation articles: Arslan et al. (2020)/Sharma et al. (2020), He et al. (2020); background reading: read Sharma et al. (2019)
07.02. Hate speech detection Presentation 1: M. Mourhaf Kazzaz, Jinghua Xu; presentation 2: Zarah Weiss; chairs 1: Nino Meisinger, Diana-Constantina Höfels; chairs 2: Markus Schoch, Belinda Deskaj Presentation articles: Gao et al. (2017); Sap et al. (2019); background reading: read Schmidt & Wiegand (2017)

Course Requirements

This is a 3–6 CP course with one lecture per week requiring active in-class participation as well as mandatory out-of-class course work. You will receive 3 CP for actively participating in the course and giving a graded presentation (see details below). Additional 3 CP can be obtained by chairing a session and passing 80% of the required quizzes (see details below). However, even if you take the course for 3 CP and do not participate in the quizzes, everyone is assumed to have read all pre-class reading materials.

You can additionally also write a hands-on term paper (at most 10 pages) on a topic of your choice including programming or statistical analyses for extra 3 CP during the semester break. Note that you need to come up with your own topic and that you are encouraged to meet with me beforehand to discuss your term paper ideas. All term papers need to be submitted by 31.03.2021. For more details, please consult the announcement forum in Moodle.

Presentation of a topic based on 1-2 papers

Chair a session and prepare a protocol for the course wiki

Note that chairing the session and writing the protocol are assumed to take more effort than the preparation of a single presentation. Your protocol is expected to reflect

  1. the contents of the papers presented
  2. the discussion in class
  3. your own thoughts and reflections on the discussion as well as the papers/input reading

and to allow others who were not in the session to understand the main points and challenges of the topic itself. While the protocol is not being graded, you may be asked to revise your protocol even after the deadline until it fulfills this requirement.

Active participation and discussion in class

Literature overview

This is a sample of literature relating to the topics we will discuss throughout class. All literature is sorted by topic. Each topic is divided into overview/introductory articles and core reading (or in some cases more fine grained sub-topics). Overview/introductory articles and some other sub-sections are marked as not available for presentations, because the papers are too general or too specific. They are listed here as background or extended reading beyond the course context.

Feel free to suggest own literature if you would like to discuss an article that is not being included in this list.


Besides the following lists, you can also present any paper from, except for papers from the sections Surveys or Social Impact of Biases.

Overview and introductory articles (not available for presentations)

Data, metrics & detection

Natural Language Generation, Understanding and Inference

Word embeddings

Coreference Resolution and Relation Extraction

Machine Translation

Sentiment Analysis

Hate Speech Detection

Bias in social media

Biases in speech recognition

POS tagging, parsing and NER




Overview and introductory articles (not available for presentations)

Core Reading List

Dual use & responsibility in AI

Overview and introductory articles (not available for presentations)

Core reading list

Ethical issues in research and the research community

Overview and introductory articles (not available for presentations)

Ethical research methods & design

Minority representation in the AI community

More papers for background (not available for presentations):

Green NLP

Overview and introductory articles (not available for presentations)

Core reading list

Conversational agents

Overview and introductory articles (not available for presentations)

Core reading list

Hate speech detection

Overview and introductory articles (not available for presentations)

Approaches to hate speech detection

Corpora and studies on hate speech

Racial bias in hate speech detection

NLP for bias and stereotype detection

Overview and introductory articles (not available for presentations)

Core reading list

Non-computational papers on language of bias and dehumanization (not available for presentations)

NLP, propaganda and misinformation

Overview and introductory articles (not available for presentations)

Fake news detection

Corpora and studies on propaganda and misinformation

Non-linguistic work on propaganda and misinformation (not available for presentations)

NLP and health

Overview and introductory articles (not available for presentations)

Core reading list