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.

• Lecture: Mondays, 4pm (c.t.)
• On-site Location: room 0.02, Wilhelmstrasse 19, Tübingen (SfS; Verfügungsgebäude) (cancelled)
• Remote Location: https://zoom.us/j/99267155454?pwd=NzZuL012T2hHMW5Tb1VNQU9nRzRRZz09
• Moodle page: https://moodle.zdv.uni-tuebingen.de/course/view.php?id=2049
• Home page: http://www.sfs.uni-tuebingen.de/~zweiss/ENLP.html
• Lecturer: Zarah Weiss
• Virtual office hours: Fridays, 10-11am, http://purl.org/zweiss/zoom

Syllabus

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

• Choose a presentation topic and article by November 8th, noon via the corresponding thread in the Moodle forum (first come, first served).
• You can find a selection of papers in the literature list below. Note that overview and introductory articles are not available as presentation topics.
• You may present in groups of up to 2 people. This does not need to be the same group with which you chair a session.
• Being a presenter does not exempt you from taking the pre-class quiz (see “active participation” below).
• Presentations can be held in two formats
  1. 20 minute presentation + 10 minute discussion/Q&A. Note: prepare a question that you would like to discuss in case no follow-up questions are being asked. Actively moderating the post-presentation discussion is part of your grade. If you present in groups of two, make sure that both of you are engaged in presenting and discussing. If there is too little engagement in the discussion, feel free to ask one of the session chairs (see below) for their contribution.
  2. 15 minute presentation + 10 minute system/code demonstration + 5 minute questions. This option is suitable, if you want to give a brief live demonstration of a programming or data analysis example, e.g., how to visualize biases in word embeddings using python.
• Your presentation slides are due on the Friday before your in-class presentation at 9am. Presentations can receive up to 30 points. Delayed submissions will lead to a reduction of 1 point every 15 minutes.
• After your presentation, answer any questions being posted regarding your topic in the Moodle forum for the remainder of the week (until Friday).

Chair a session and prepare a protocol for the course wiki

• As a session chair, you will be responsible for making a protocol of the session and actively participating in all discussions.
• Choose a session to chair by November 8th, noon via the corresponding thread in the Moodle forum (first come, first served).
• You may chair a session in groups of up to 2 people. This does not need to be the same group in which you presented.
• You cannot chair the session in which you are presenting.
• Being session chair does not exempt you from taking the pre-class quiz (see “active participation” below).
• Chairing a session entails the following tasks:
  1. Preparation
     • Carefully read the papers for the lecture you are chairing
     • Each member of the chairing group writes 200-300 words for each of the articles being presented containing your thoughts on the respective article. Don’t summarize the article. Instead, highlight arguments that you found novel/compelling or unconvincing/impractical and or formulate questions based on what did not become clear to you when reading the article.
     • Post your paragraph in the glossary on Moodle before the session.
  2. During the presentations
     • Each member of the chairing group should be ready to offer their opinion, thoughts, questions in the in-class discussion after each presentation at any time. If the discussion is not going smoothly, presenters are encouraged to call on you to get a discussion running, even if you are not raising your hand.
     • As a group, write down the main questions and discussion points during the post-presentation discussion.
  3. During the group discussion phase
     • As a group, take notes on the main discussion points during the group discussion.
     • Write down any open questions that remain after the discussion.
  4. After the lecture
     • As a group, use your collective notes to write a 1.000-1.500 word wiki entry and post it in the Moodle glossary before the next lecture starts. Feel free to re-use parts of the texts that you wrote in preparation of the session, but also make sure to augment them with the additional information gained in class during the presentations and discussion.
     • The glossary entry should reflect the core concepts, facts and discussion points of the lecture and be accessible for anyone wanting to read up on what we talked about in the session later on.

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

• You are required to read the article(s) for each presentation and prepare the additional discussion materials.
• You need to pass a weekly quiz of 10 true/false questions that is available on Moodle.
• The quiz is available until the start of the lecture, but you can take it 3 times (best trial counts) and without time limitation.
• To pass a quiz, you need to answer 60% of all questions correctly (= 6 questions).
• To get full course credit, you need to pass at least 80% of quizzes.
• You are encouraged to ask any questions you have before or after class in the Moodle forum or directly 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.

Bias

Besides the following lists, you can also present any paper from https://github.com/uclanlp/awesome-fairness-papers, except for papers from the sections Surveys or Social Impact of Biases.

Overview and introductory articles (not available for presentations)

• Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020). Language (technology) is power: A critical survey of “bias” in NLP. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5454–5476.
• Crawford, K. (2017): The Trouble with Bias. NIPS 2017 Keynote on YouTube.
• Hovy, D., & Prabhumoye, S. (2021). Five sources of bias in natural language processing. Language and Linguistics Compass, 15(8), e12432.
• Loukina, A., Madnani, N., & Zechner, K. (2019). The many dimensions of algorithmic fairness in educational applications. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications (pp. 1–10). Association for Computational Linguistics.
• Sun, Tony, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. 2019. Mitigating gender bias in natural language processing: Literature review. ACL 2019
• Tatman, R. (2017). Gender and Dialect Bias in YouTube’s Automatic Captions. In D. Hovy, S. Spruit, M. Mitchell, E. M. Bender, M. Strube, & H. Wallach (Chairs), Proceedings of the First ACL Workshop on Ethics in Natural Language Processing. Retrieved from https://www.aclweb.org/anthology/W17-1606.pdf
• Zhou, P., Shi, W., Zhao, J., Huang, K. H., Chen, M., Cotterell, R., & Chang, K. W. (2019). Examining gender bias in languages with grammatical gender. arXiv preprint arXiv:1909.02224.

Data, metrics & detection

• Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science (New York, N.Y.), 356(6334), 183–186. Retrieved from xhttps://arxiv.org/pdf/1608.07187.pdf
• Dhamala, J., Sun, T., Kumar, V., Krishna, S., Pruksachatkun, Y., Chang, K. W., & Gupta, R. (2021, March). Bold: Dataset and metrics for measuring biases in open-ended language generation. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 862-872).
• Vinodkumar Prabhakaran, Ben Hutchinson, Margaret Mitchell. 2019. Perturbation Sensitivity Analysis to Detect Unintended Model Biases. EMNLP 2019.
• Mattia Samory, Indira Sen, Julian Kohne, Fabian Floeck, Claudia Wagner. 2020. “Unsex me here”: Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples. Arxiv
• Shah, D., Schwartz, H. A., & Hovy, D. (2019). Predictive biases in natural language processing models: A conceptual framework and overview. arXiv preprint arXiv:1912.11078.
• Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, Yoav Goldberg. 2020. Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection. ACL 2020
• Moin Nadeem, Anna Bethke, and Siva Reddy. 2020. StereoSet: Measuring stereotypical bias in pretrained language models. Arxiv
• Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, Stuart Shieber. 2020. Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias
• Rob Voigt, David Jurgens, Vinodkumar Prabhakaran, Dan Jurafsky, and Yulia Tsvetkov. 2018. RtGender: A Corpus of Responses to Gender for Studying Gender Bias. LREC 2018
• Wang, T., Zhao, J., Yatskar, M., Chang, K. W., & Ordonez, V. (2019). Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 5310-5319).
• Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge. 2018. Mind the GAP: A balanced corpus of gendered ambiguous pronouns. TACL.
• Zhao, J., & Chang, K. W. (2020). LOGAN: Local Group Bias Detection by Clustering. arXiv preprint arXiv:2010.02867.
• Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017). Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 2979–2989). Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/D17-1323.pdf
• Zhong, Ruiqi, Yanda Chen, Desmond Patton, Charlotte Selous, and Kathy McKeown. “Detecting and Reducing Bias in a High Stakes Domain.” arXiv preprint arXiv:1908.11474(2019).
• R Zmigrod, SJ Mielke, H Wallach, R Cotterell. 2019. Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology arXiv:1906.04571, 2019.

Natural Language Generation, Understanding and Inference

• Rachel Rudinger, Chandler May, and Benjamin Van Durme. 2017. Social bias in elicited natural language inferences. In ACL Workshop on Ethics in NLP, pages 74–79.
• Sharma, S., Dey, M., & Sinha, K. (2020). Evaluating Gender Bias in Natural Language Inference. In: NeurIPS 2020 Workshop on Dataset Curation and Security.
• Sheng, E., Chang, K. W., Natarajan, P., & Peng, N. (2021). Societal Biases in Language Generation: Progress and Challenges. arXiv preprint arXiv:2105.04054.
• Sheng, E., Chang, K. W., Natarajan, P., & Peng, N. (2020). Towards controllable biases in language generation. arXiv preprint arXiv:2005.00268.
• Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019). The Woman Worked as a Babysitter: On Biases in Language Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3405–3410). Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/D19-1339.pdf
• Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych. 2020. Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance. ACL 2020

Word embeddings

• Oshin Agarwal, Funda Durupınar, Norman I. Badler, and Ani Nenkova. 2019. Word embeddings (also) encode human personality stereotypes. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
• Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In Proceedings of the 30th International Conference on Neural Information Processing Systems. Retrieved from http://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings.pdf
• Kawin Ethayarajh, David Duvenaud, and Graeme Hirst. 2019. Understanding undesirable word embedding associations. ACL 2019.
• Gonen, H., & Goldberg, Y. (2019). Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them. arXiv preprint arXiv:1903.03862.
• Kaneko, M., & Bollegala, D. (2019). Gender-preserving debiasing for pre-trained word embeddings. arXiv preprint arXiv:1906.00742.
• Vaibhav Kumar, Tenzin Singhay Bhotia, Vaibhav Kumar, Tanmoy Chakraborty. 2020. Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings. TACL.
• Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019. Measuring bias in contextualized word representations. In Proceedings of the First Workshop on Gender Bias in Natu- ral Language Processing, pages 166–172, Florence, Italy. Association for Computational Linguistics.
• Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black and Yulia Tsvetkov. 2019. Quantifying Social Biases in Contextual Word Representations. Proc. of Workshop on Gender Bias for NLP
• Thomas Manzini, Yao Chong, Yulia Tsvetkov and Alan W Black. 2019. Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings. NAACL 2019.
• Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, Rachel Rudinger. On Measuring Social Biases in Sentence Encoders. NAACL 2019.
• Papakyriakopoulos, Orestis, Simon Hegelich, Juan Carlos Medina Serrano, and Fabienne Marco. “Bias in word embeddings.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 446-457. 2020.
• Candace Ross, Boris Katz, Andrei Barbu. 2020. Measuring Social Biases in Grounded Vision and Language Embeddings. https://arxiv.org/abs/2002.08911
• Yi Chern Tan and L Elisa Celis. 2019. Assessing social and intersectional biases in contextualized word representations. In Advances in Neural Information Processing Systems, pages 13209–13220, 2019.
• Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordonez, V., & Chang, K. W. (2019). Gender bias in contextualized word embeddings. arXiv preprint arXiv:1904.03310.
• Zhao, J., Zhou, Y., Li, Z., Wang, W., & Chang, K. W. (2018). Learning gender-neutral word embeddings. arXiv preprint arXiv:1809.01496.

Coreference Resolution and Relation Extraction

• Yang Trista Cao, Hal Daumé III. 2019. Toward Gender-Inclusive Coreference Resolution
• Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang. 2019. Towards Understanding Gender Bias in Relation Extraction.
• Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018. Gender bias in coreference resolution. In NAACL.
• Zhao, Jieyu, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Gender bias in coreference resolution: Evaluation and debiasing methods." NAACL 2018

Machine Translation

• Hila Gonen and Kellie Webster. 2020. Automatically Identifying Gender Issues in Machine Translation using Perturbations. Arxiv
• Prates, M. O. R., Avelar, P. H., & Lamb, L. C. (2019). Assessing gender bias in machine translation: A case study with Google Translate. Neural Computing and Applications, 14(1). Retrieved from https://arxiv.org/pdf/1809.02208.pdf
• Stanovsky, G., Smith, N. A., & Zettlemoyer, L. (2019). Evaluating Gender Bias in Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/P19-1164.pdf
• Danielle Saunders and Bill Byrne. 2020. Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem. ACL 2020

Sentiment Analysis

• Bhaskaran, J., & Bhallamudi, I. (2019). Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing (pp. 62–68). Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/W19-3809.pdf
• Svetlana Kiritchenko, Saif M. Mohammad. 2018. Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems. Workshop on Ethics in NLP 2018. https://arxiv.org/pdf/1805.04508.pdf

Hate Speech Detection

• Davidson, T., Bhattacharya, D., & Weber, I. (2019). Racial Bias in Hate Speech and Abusive Language Detection Datasets. In Proceedings of the Third Workshop on Abusive Language Online (pp. 25–35). Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/W19-3504.pdf
• Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019). The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 1668–1678). Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/P19-1163.pdf

Bias in social media

• S. L. Blodgett, B. O’Connor. 2017. Racial disparity in natural language processing: A case study of social media African-American English. in Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Workshop (KDD, 2017).
• Blodgett, Su Lin, Lisa Green, and Brendan O’Connor. 2016. Demographic dialectal variation in social media: A case study of African-American English. EMNLP 2016
• Ferrer, Xavier, van Nuenen, Tom, Such, Jose M., and Criado, Natalia, 2021. Discovering and categorising language biases in Reddit. Proceedings of ICWSM.

Biases in speech recognition

• Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John Rickford, Dan Jurafsky, and Sharad Goel. 2020. Racial Disparity in Automated Speech Recognition. Proceedings of the National Academy of Sciences. Retrieved from https://www-pnas-org.stanford.idm.oclc.org/content/early/2020/03/17/1915768117
• R. Tatman, C. Kasten, “Effects of talker dialect, gender and race on accuracy of Bing speech and YouTube automatic captions” in INTERSPEECH (2017), pp. 934–938.
• Sen and Wasow (2016) Race as a Bundle of Sticks: Designs that Estimate Effects of Seemingly Immutable Characteristics, Annual Review of Political Science

POS tagging, parsing and NER

• Deven Shah, H. Andrew Schwartz, Dirk Hovy. 2020. Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview.
• Garimella, Aparna, Carmen Banea, Dirk Hovy, and Rada Mihalcea. Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing. ACL 2019.
• Ninareh Mehrabi, Thamme Gowda, Fred Morstatter, Nanyun Peng, Aram Galstyan. 2019. Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition.

Fairness

• Pruksachatkun, Y., Krishna, S., Dhamala, J., Gupta, R., & Chang, K. W. (2021). Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification. arXiv preprint arXiv:2106.10826.
• Kawin Ethayarajh. 2020. Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds. ACL 2020

Other

• Hila Gonen, Yova Kementchedjhieva, Yoav Goldberg. 2019. How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages? CoNLL 2019. https://arxiv.org/abs/1910.14161
• Ü Pei Zhou, Weijia Shi, Jieyu Zhao, Kuan-Hao Huang, Muhao Chen, Ryan Cotterell and Kai-Wei Chang. 2019. Examining Gender Bias in Languages with Grammatical Gender. EMNLP-IJCNLP 2019.
• Noble, S. U. (2018): Algorithms of Oppression. How Search Engines Reinforce Racism. NYU Press.
  Proceedings of the First ACL Workshop on Ethics in Natural Language Processing: https://www.degruyter.com/document/doi/10.18574/9781479833641/html

Privacy

Overview and introductory articles (not available for presentations)

• Cynthia Dwork & Deirdre K. Mulligan: It’s Not Privacy, and It’s Not Fair. https://www.stanfordlawreview.org/online/privacy-and-big-data-its-not-privacy-and-its-not-fair/
• Akela Lacy, Alice Speri, Jordan Smith, Sam Biddle. 2020. Prisons launch “absurd” attempt to detect coronavirus in inmate phone calls. The Intercept, April 21, 2020.
• Lewis, D., Moorkens, J., & Fatema, K. (2017, April). Integrating the management of personal data protection and open science with research ethics. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (pp. 60-65). https://aclanthology.org/W17-1607.pdf
• Fabiano, N., & Fabiano, S. L. (2019). Ethics and the protection of personal data. paragraph, 1, 3.
• Shoshana Zuboff. The Age of Surveillance Capitalism. Selections.

Core Reading List

• Ahmad, W. U., Chang, K. W., & Wang, H. (2018, June). Intent-aware query obfuscation for privacy protection in personalized web search. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 285-294).
• Boyd, Danah, and Alice E. Marwick. “Social privacy in networked publics: Teens’ attitudes, practices, and strategies.” In A decade in internet time: Symposium on the dynamics of the internet and society. 2011. https://osf.io/2gec4/download
• Coavoux, Maximin, Shashi Narayan, and Shay B. Cohen. 2018. "Privacy-preserving neural representations of text." EMNLP 2018
• Hovy, D. (2015). Demographic factors improve classification performance. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (volume 1: Long papers) (pp. 752-762)
• David Jurgens, Yulia Tsvetkov, and Dan Jurafsky. 2017. Writer Profiling Without the Writer’s Text.
• Lima-López, S., Perez, N., García-Sardiña, L., & Cuadros, M. (2020, May). HitzalMed: Anonymisation of Clinical Text in Spanish. In Proceedings of the 12th Language Resources and Evaluation Conference (pp. 7038-7043).
• Rangel, F., Rosso, P., Potthast, M., & Stein, B. (2017). Overview of the 5th author profiling task at pan 2017: Gender and language variety identification in twitter. Working notes papers of the CLEF, 1613-0073.
• Yoav Goldberg (2018) 4gram language models share secrets too…, Github.

Dual use & responsibility in AI

Overview and introductory articles (not available for presentations)

• Alfano, M., Sullivan, E., & Fard, A. E. Ethical pitfalls for natural language processing in psychology.
  Pasquale,F. & Citrone, D. (2014): The Scored Society: Due Process for Automated Predictions. Washington Law Review, 89(1).
• Dignum, V. (2019). Responsible artificial intelligence: how to develop and use Ai in a responsible way. Cham, Switzerland: Springer.
• Pasquale, F. (2016): Black Box Society: The Secret Algorithms that Control Money and Information. Harvard University Press.
• O’Neil, C. (2016): Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books.
• Hovy, D., & Spruit, S. L. (2016). The Social Impact of Natural Language Processing. In K. Erk & N. A. Smith (Chairs), Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany. Retrieved from https://www.aclweb.org/anthology/P16-2096.pdf
• Wang, Y., & Kosinski, M. (2018). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of personality and social psychology, 114(2), 246.

Core reading list

• Floridi, L. (2016). Faultless responsibility: on the nature and allocation of moral responsibility for distributed moral actions, Philosophical Transactions of the Royal Society A (Mathematical Physical and Engineering Sciences), 374 (2083); doi: 10.1098/rsta.2016.0112.
• Henderson, Peter, Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, and Joelle Pineau. 2018. "Ethical challenges in data-driven dialogue systems." In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 123-129. ACM, 2018.
• Johannen, D., Biemann, C., & Scheffer, D. (2020). Ethical considerations of the GermEval20 Task 1. IQ assessment with natural language processing: Forbidden research or gain of knowledge. Proceedings of the GermEval 2020 Task, 1.
• Johnson, D. G. & T. M. Power (2005). Computer systems and responsibility: A normative look at technological complexity, Ethics and Information Technology, 7: 99–107.
• Matthias, A., (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata, Ethics and Information Technology, 6: 175–183.
• Nyholm, S. (2018). The Ethics of Crashes with Self-Driving Cars: A Roadmap, II, Philosophy Compass, 13(7): e12506. doi: 10.1111/phc3.12506.
• Tsarapatsanis, D., & Aletras, N. (2021). On the Ethical Limits of Natural Language Processing on Legal Text. arXiv preprint arXiv:2105.02751.

Ethical issues in research and the research community

Overview and introductory articles (not available for presentations)

• Emily M. Bender and Batya Friedman. 2018. Data statements for NLP: Toward mitigating system bias and enabling better science. TACL 6, 587–604.
• Parra Escartín, C., Reijers, W., Lynn, T., Moorkens, J., Way, A., & Liu, C. H. (2017). Ethical considerations in NLP shared tasks. Association for Computational Linguistics. http://doras.dcu.ie/23231/1/Ethical Considerations in NLP Shared Tasks.pdf
• Leidner, J. L., & Plachouras, V. (2017, April). Ethical by design: Ethics best practices for natural language processing. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (pp. 30-40). https://aclanthology.org/W17-1604.pdf
• Leins, K., Lau, J. H., & Baldwin, T. (2020). Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis? arXiv preprint arXiv:2005.13213.
• Shuster, Evelyne. 1997. Fifty years later: the significance of the Nuremberg Code." New England Journal of Medicine 337, 20: 1436-1440.
• Tsarapatsanis, D., & Aletras, N. (2021). On the Ethical Limits of Natural Language Processing on Legal Text. arXiv preprint arXiv:2105.02751.

Ethical research methods & design

• John W Ayers, Theodore L Caputi, Camille Nebeker, Mark Dredze. Don’t quote me: reverse identification of research participants in social media studies. Nature Digital Medicine, 2018. https://www.nature.com/articles/s41746-018-0036-2
• Bianchi, F., & Hovy, D. (2021). On the gap between adoption and understanding in NLP. Findings of the Association for Computational Linguistics: ACL-IJCNLP, 2021, 3895-3901.
• Casey Fiesler and Nicholas Proferes. 2018. “Participant” Perceptions of Twitter Research Ethics. Social Media + Society, 4(1). 22
• Fort, K., & Couillault, A. (2016). Yes, We Care! Results of the Ethics and Natural Language Processing Surveys. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16). Retrieved from https://www.aclweb.org/anthology/L16-1252
• R. Stuart Geiger, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, Jenny Huang. 2020. Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From? ACM FAT* 2020
• Divyansh Kaushik, Eduard Hovy, Zachary C. Lipton. (2019). Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
• Larson, B. N. (2017). Gender as a variable in natural-language processing: Ethical considerations. https://scholarship.law.tamu.edu/cgi/viewcontent.cgi?referer=https://scholar.google.de/&httpsredir=1&article=1831&context=facscholar
• Läubli, Samuel, Sheila Castilho, Graham Neubig, Rico Sennrich, Qinlan Shen, and Antonio Toral. “A Set of Recommendations for Assessing Human–Machine Parity in Language Translation.” Journal of Artificial Intelligence Research 67 (2020): 653-672.
• Lipton, Zachary C., and Jacob Steinhardt. 2019. “Troubling trends in machine learning scholarship.” Queue 17, no. 1 (2019): 45-77
• Madnani, N., Loukina, A., Von Davier, A., Burstein, J., & Cahill, A. (2017, April). Building better open-source tools to support fairness in automated scoring. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (pp. 41-52). https://aclanthology.org/W17-1605.pdf
• Mieskes, M. (2017, April). A quantitative study of data in the NLP community. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 23-29). https://aclanthology.org/W17-1603.pdf
• Shmueli, B., Fell, J., Ray, S., & Ku, L. W. (2021). Beyond fair pay: Ethical implications of NLP crowdsourcing. arXiv preprint arXiv:2104.10097.
• Vitak, J., Shilton, K., Beyond the Belmont principles: Ethical challenges, practices, and beliefs in the online data research community. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing (pp. 941-953).
• Williams, M. L., Burnap, P., Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation. Sociology, 51(6), 1149–1168.

Minority representation in the AI community

• Cheong, M., Leins, K., & Coghlan, S. (2021). Computer Science Communities: Who is Speaking, and Who is Listening to the Women? Using an Ethics of Care to Promote Diverse Voices. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 106-115).
• Koolen, C., & van Cranenburgh, A. (2017, April). These are not the stereotypes you are looking for: bias and fairness in authorial gender attribution. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (pp. 12-22). https://aclanthology.org/W17-1602.pdf
• Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 205395171667967. Retrieved from
  https://journals.sagepub.com/doi/pdf/10.1177/2053951716679679
• Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhur. 2020. The State and Fate of Linguistic Diversity and Inclusion in the NLP World. ACL 2020
• Rickford, John Russell. “Unequal partnership: Sociolinguistics and the African American speech community.” Language in Society 26, no. 2 (1997): 161-197.
• Schluter, N. (2018). The glass ceiling in NLP. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 2793-2798). https://www.aclweb.org/anthology/D18-1301.pdf
• L. Winner. 1980. “Do Artifacts have Politics?”, Daedalus,109 (1): 121-136

More papers for background (not available for presentations):

• Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith. 2019. Show Your Work: Improved Reporting of Experimental Results In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
• Crane, M. (2018). Questionable Answers in Question Answering Research: Reproducibility and Variability of Published Results. Transactions of the Association for Computational Linguistics, 6, 241–252.
• John Ioannidis (2005) Why most published scientific results are false. PLOS Medicine 2:e124
• Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, Noah A. Smith. 2020. Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping. arXiv, 2020
• Melis, Gábor, Chris Dyer, and Phil Blunsom. 2018. On the state of the art of evaluation in neural language models. ICLR
• Joseph P. Simmons, Leif D. Nelson, Uri Simonsohn. 2011. False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science 22:11, 1359-1366.
• McCoy, T., Pavlick, E., and Linzen, T. (2019). Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3428–3448
• Rotem Dror, Lotem Peled-Cohen, Segev Shlomov and, Roi Reichart. 2020. “Statistical Significance Testing for Natural Language Processing.” Morgan Claypool Human Language Technology series.
• Drew McDermott. 1976. Artificial Intellgience Meets Natural Stupidity. ACM SIGART Bulletin, (57), 4-9.
• Ai, Hua, Antoine Raux, Dan Bohus, Maxine Eskenazi, and Diane Litman. 2007. Comparing spoken dialog corpora collected with recruited subjects versus real users." SIGdial 2007, pp. 124-131.
• Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hanna Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020. Evaluating NLP Models via Contrast Sets.
• Sugawara, Saku, Pontus Stenetorp, Kentaro Inui, and Akiko Aizawa. 2020. Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets. Arxiv
• Antonio Toral. 2020. Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019. EAMT 2020.

Green NLP

Overview and introductory articles (not available for presentations)

• Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren Etzioni. 2019. Green AI. Communications of the ACM (CACM),

Core reading list

• Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency March 2021 Pages 610–623.
• Conforti, C., Hirmer, S., Morgan, D., Basaldella, M., & Or, Y. B. (2020). Natural language processing for achieving sustainable development: the case of neural labelling to enhance community profiling. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. 8427–8444. https://aclanthology.org/2020.emnlp-main.677.pdf
• Ethayarajh, K., & Jurafsky, D. (2020). Utility is in the eye of the user: A critique of NLP leaderboards. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, p. 4846–4853. https://aclanthology.org/2020.emnlp-main.393.pdf
• Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau. 2020. Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. Arxiv.
• Maronikolakis, A., & Schütze, H. (2021, April). Multidomain Pretrained Language Models for Green NLP. In Proceedings of the Second Workshop on Domain Adaptation for NLP (pp. 1-8).
• Emma Strubell, Ananya Ganesh and Andrew McCallum. 2019. Energy and Policy Considerations for Deep Learning in NLP. ACL 2019.
• Zhou, X., Chen, Z., Jin, X., & Wang, W. Y. (2020). Hulk: An energy efficiency benchmark platform for responsible natural language processing. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, p. 329–336. https://aclanthology.org/2021.eacl-demos.39.pdf

Conversational agents

Overview and introductory articles (not available for presentations)

• Henderson, Peter, Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, and Joelle Pineau. 2018. Ethical challenges in data-driven dialogue systems. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 123-129. ACM, 2018.
• Neff, Gina, and Peter Nagy. 2016. “Automation, algorithms, and politics talking to Bots: Symbiotic agency and the case of Tay.” International Journal of Communication 10 (2016): 17.
• Schlesinger, Ari, Kenton P. O’Hara, and Alex S. Taylor. 2018. Let’s talk about race: Identity, chatbots, and AI. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p. 315. ACM, 2018.
• Thieltges, A., Schmidt, F., & Hegelich, S. (2016, March). The devil’s triangle: Ethical considerations on developing bot detection methods. In 2016 AAAI Spring Symposium Series.
• West, M., Kraut, R., & Ei Chew, H. (2019). I’d blush if I could: closing gender divides in digital skills through education. Unesco Report. https://unesdoc.unesco.org/ark:/48223/pf0000367416.page=1

Core reading list

• Chin, Hyojin, Lebogang Wame Molefi, and Mun Yong Yi. 2020. “Empathy Is All You Need: How a Conversational Agent Should Respond to Verbal Abuse.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-13. 2020.
• Curry, Amanda Cercas, and Verena Rieser. 2018. "# MeToo Alexa: How Conversational Systems Respond to Sexual Harassment." In Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing, pp. 7-14. 2018
• Feine, J., Gnewuch, U., Morana, S., & Maedche, A. (2019, November). Gender bias in chatbot design. In International Workshop on Chatbot Research and Design (pp. 79-93). Springer, Cham. https://www.researchgate.net/profile/Jasper-Feine/publication/337403025_Gender_Bias_in_Chatbot_Design/links/5e256c5da6fdcc1015786d19/Gender-Bias-in-Chatbot-Design.pdf
• Liu, Chia-Wei, Ryan Lowe, Iulian V. Serban, Michael Noseworthy, Laurent Charlin, and Joelle Pineau. 2016. How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation. EMNLP 2016.
• Winkle, K., Melsión, G. I., McMillan, D., & Leite, I. (2021, March). Boosting Robot Credibility and Challenging Gender Norms in Responding to Abusive Behaviour: A Case for Feminist Robots. In Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (pp. 29-37). https://dl.acm.org/doi/pdf/10.1145/3434074.3446910

Hate speech detection

Overview and introductory articles (not available for presentations)

• Fabienne H. Baider and Anna Bobori. 2020. Mitigating the frame SEXUAL THREAT in anti-migration discourse online In Darja Fišer and Philippa Smith, editors: The Dark Side of Digital Platforms: Linguistic Investigations of Socially Unacceptable Online Discourse Practices.
• Cheng, Justin, Michael Bernstein, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. 2017. "Anyone can become a troll: Causes of trolling behavior in online discussions." In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing, pp. 1217-1230. 2017.
• Jane, Emma A. 2017. "Gendered cyberhate, victim-blaming, and why the internet is more like driving a car on a road than being naked in the snow." In Cybercrime and its victims, pp. 61-78. Routledge, 2017.
• Poletto, F., Basile, V., Sanguinetti, M., Bosco, C., & Patti, V. (2021). Resources and benchmark corpora for hate speech detection: a systematic review. Language Resources and Evaluation, 55(2), 477-523. https://link.springer.com/content/pdf/10.1007/s10579-020-09502-8.pdf
• David Jurgens, Libby Hemphill and Eshwar Chandrasekharan. 2019. A Just and Comprehensive Strategy for Using NLP to Address Online Abuse. ACL
• Schmidt, Anna, and Michael Wiegand. 2017. "A survey on hate speech detection using natural language processing." In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, pp. 1-10. 2017.

Approaches to hate speech detection

• Luke Breitfeller, Emily Ahn, Aldrian Obaja Muis, David Jurgens and Yulia Tsvetkov. 2019. Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts. EMNLP-IJCNLP 2019.
• Lana Cuthbertson, Alex Kearney, Riley Dawson, Ashia Zawaduk, Eve Cuthbertson, Ann Gordon-Tighe, Kory W Mathewson. 2019. Women, politics and Twitter: Using machine learning to change the discourse. NeurIPS Joint Workshop on AI for Social Good at NeurIPS 2019
• Founta, Antigoni Maria, Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Athena Vakali, and Ilias Leontiadis. 2019. "A unified deep learning architecture for abuse detection." In Proceedings of the 10th ACM Conference on Web Science, pp. 105-114. 2019.
• Gao, Lei, Alexis Kuppersmith, and Ruihong Huang. 2017. Recognizing explicit and implicit hate speech using a weakly supervised two-path bootstrapping approach. IJCNLP 2017.
• Ping Liu, Joshua Guberman, Libby Hemphill, and Aron Culotta. 2018. Forecasting the presence and intensity of hostility on instagram using linguistic and social features. ICWSM
• Serra Sinem Tekiroglu, Yi-Ling Chung, Marco Guerini. 2020. Generating Counter Narratives against Online Hate Speech: Data and Strategies. ACL 2020.
• William Warner and Julia Hirschberg. 2012. Detecting Hate Speech on the World Wide Web. Proceedings of the Second Workshop on Language in Social Media.
• Waseem, Zeerak, Thomas Davidson, Dana Warmsley, and Ingmar Weber. Understanding Abuse: A Typology of Abusive Language Detection Subtasks.. In Proceedings of the First Workshop on Abusive Language Online, pp. 78-84. 2017.
• Justine Zhang, Jonathan P. Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Nithum Thain, Dario Taraborelli. 2018. Conversations Gone Awry: Detecting Early Signs of Conversational Failure. Proceedings of ACL 2018.

Corpora and studies on hate speech

• Y.L. Chung, E. Kuzmenko, S.S. Tekiroglu, M. Guerini. 2019. CONAN – COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech. ACL
• Thomas Davidson and Debasmita Bhattacharya. 2020. Examining Racial Bias in an Online Abuse Corpus with Structural Topic Modeling. ICWSM 2020
• Mai ElSherief, Shirin Nilizadeh, Dana Nguyen, Giovanni Vigna, and Elizabeth Belding. 2018. Peer to peer hate: Hate speech instigators and their targets. ICWSM.
• Sweta Karlekar and Mohit Bansal. 2018 SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories. Proceedings of EMNLP 2018, Brussels, Belgium
• Zijian Wang and Christopher Potts. 2019. TalkDown: a corpus for condescension detection in context. In EMNLP.
• Mathew, Binny, Punyajoy Saha, Hardik Tharad, Subham Rajgaria, Prajwal Singhania, Suman Kalyan Maity, Pawan Goyal, and Animesh Mukherjee. 2019. Thou shalt not hate: Countering online hate speech. In Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, no. 01, pp. 369-380. 2019.
• Munger, Kevin. 2017. Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment. Political Behavior 39(3):629–649.
• Jing Qian, Anna Bethke, Yinyin Liu, Elizabeth Belding, and William Yang Wang. 2019. A benchmark dataset for learning to intervene in online hate speech. EMNLP.

Racial bias in hate speech detection

• Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, Noah A. Smith. 2019. The Risk of Racial Bias in Hate Speech Detection. ACL 2019.
• Mengzhou Xia, Anjalie Field, Yulia Tsvetkov. 2020. Demoting Racial Bias in Hate Speech Detection. SocialNLP Workshop at ACL 2020.
• Wiegand, Michael, Josef Ruppenhofer, and Thomas Kleinbauer. 2019. Detection of abusive language: the problem of biased datasets. NAACL19

NLP for bias and stereotype detection

Overview and introductory articles (not available for presentations)

• Elliott Ash, Daniel L. Chen, Arianna Ornaghi. 2020. Stereotypes in High-Stakes Decisions: Evidence from U.S. Circuit Courts. NBER Manuscript.
• Nikhil Garg, Londa Schiebinger, Dan Jurafsky, James Zou. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences 2018.
• Haslam, N. (2006). Dehumanization: An Integrative Review. Personality and Social Psychology Review (Vol. 10).
• Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 26(3), 1771-1796. https://link.springer.com/article/10.1007/s11948-020-00213-5

Core reading list

• Serina Chang, Kathy McKeown. 2019. Automatically Inferring Gender Associations from Language. EMNLP 2019
• Fast, Ethan, Tina Vachovsky, and Michael S. Bernstein. 2016. Shirtless and dangerous: Quantifying linguistic signals of gender bias in an online fiction writing community. In Tenth International AAAI Conference on Web and Social Media. 2016.
• Field, Anjalie, Gayatri Bhat, and Yulia Tsvetkov. 2019. Contextual Affective Analysis: A Case Study of People Portrayals in Online #MeToo Stories. ICWSM (2019)
• Field, Anjalie and Yulia Tsvetkov. 2019. "Entity-Centric Contextual Affective Analysis". ACL 2019
• Sap, Maarten, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. 2020. Social Bias Frames: Reasoning about Social and Power Implications of Language
• Scott Friedman, Sonja Schmer-Galunder, Jeffrey Rye, Robert Goldman, and Anthony Chen. 2019. Relating Linguistic Gender Bias, Gender Values, and Gender Gaps: An International Analysis.
• Hoyle, Alexander Miserlis, Lawrence Wolf-Sonkin, Hanna Wallach, Isabelle Augenstein, and Ryan Cotterell. 2019. Unsupervised Discovery of Gendered Language through Latent-Variable Modeling. ACL 2019.
• Joseph, Kenneth, Wei Wei, and Kathleen M. Carley. “Girls rule, boys drool: Extracting semantic and affective stereotypes from Twitter.” ACM CSCW 1362-1374. ACM, 2017.
• Liye Fu, Cristian Danescu-Niculescu-Mizil, and Lillian Lee. 2016. Tie-breaker: Using language models to quantify gender bias in sports journalism. In Proceedings of the IJCAI workshop on NLP meets Journalism.
• Kenneth Joseph and Jonathan H. Morgan. 2020. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? ACL2020
• Koolen, C., & van Cranenburgh, A. (2017). These are not the stereotypes you are looking for: bias and fairness in authorial gender attribution. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (pp. 12-22). https://aclanthology.org/W17-1602.pdf
• Navid Rekabsaz, James Henderson, Robert West, Allan Hanbury. 2020. Measuring Societal Biases in Text Corpora via First-Order Co-occurrence. Arxiv
• Patrick Schramowski, Cigdem Turan, Sophie Jentzsch, Constantin Rothkopf and Kristian Kersting. 2020. BERT has a Moral Compass: Improvements of ethical and moral values of machines.
• Rob Voigt, Nicholas P. Camp, Vinodkumar Prabhakaran, William L. Hamilton, Rebecca C. Hetey, Camilla M. Griffiths, David Jurgens, Dan Jurafsky, and Jennifer L. Eberhardt. 2017. Language from police body camera footage shows racial disparities in officer respect. PNAS

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

• Goff, Phillip Atiba, Jennifer L. Eberhardt, Melissa J. Williams, and Matthew Christian Jackson. Not yet human: implicit knowledge, historical dehumanization, and contemporary consequences. Journal of personality and social psychology94, no. 2 (2008): 292. [Language study is study 6 (page 303-304)]
• Susan Tyler Eastman, Andrew C. Billings (2001) Biased Voices of Sports: Racial and Gender Stereotyping in College Basketball Announcing, Howard Journal of Communications, 12:4, 183-201, DOI: 10.1080/106461701753287714
• James A. Rada and K. Tim Wulfemeyer (2005) Color Coded: Racial Descriptors in Television Coverage of Intercollegiate Sports, Journal of Broadcasting and Electronic Media, 49:1, 65-85, DOI: 10.1207/s15506878jobem4901_5
• Santa Ana, Otto. Brown tide rising: Metaphors of Latinos in contemporary American public discourse. University of Texas Press, 2002.
• Haslam, N., Bain, P., Douge, L., Lee, M., and Bastian, B. (2005). More Human Than You: Attributing Humanness to Self and Others.
• Haslam, N., Loughnan, S., and Sun, P. (2006). Beastly: What Makes Animal Metaphors Offensive? Journal of Language and Social Psychology, 30(3), 311–325.
• Haslam, N., Rothschild, L., and Ernst, D. (2000). Essentialist beliefs about social categories. British Journal of Social Psychology, 39(1), 113–127. https://doi.org/10.1348/014466600164363
• Leyens, J.-P., Rodriguez-Perez, A., Rodriguez-Torres, R., Gaunt, R., Paladino, M.-P., Vaes, J., and Phanie Demoulin, S. (2001). Psychological essentialism and the differential attribution of uniquely human emotions to ingroups and outgroups. European Journal of Social Psychology Eur. J. Soc. Psychol, 31, 395–411
• Morton, T. A., Postmes, T., Haslam, S. A., and Hornsey, M. J. (2009). Theorizing gender in the face of social change: Is there anything essential about essentialism? Journal of Personality and Social Psychology, 96(3), 653–664.
• Waytz, A., Hoffman, K. M., and Trawalter, S. A Superhumanization Bias in Whites’ Perceptions of Blacks.
• Williams, M. J., and Eberhardt, J. L. (2008). Biological Conceptions of Race and the Motivation to Cross Racial Boundaries.
• Epley, N., Waytz, A., and Cacioppo, J. T. (2007). On Seeing Human: A Three-Factor Theory of Anthropomorphism.
• Bastian, B., Denson, T. F., and Haslam, N. (2013). The Roles of Dehumanization and Moral Outrage in Retributive Justice. PLoS ONE, 8(4), 61842.
• Formanowicz, M., Goldenberg, A., T et al. 2018. Understanding dehumanization: The role of agency and communion.
• Harris, L. T., and Fiske, S. T. (2006). Dehumanizing the Lowest of the Low. Psychological Science, 17(10), 847–853.
• Santa Ana, Otto (1999) Like an Animal I was Treated’: Anti-Immigrant Metaphor in US Public Discourse
• Gerald V. O’Brien (2003) Indigestible Food, Conquering Hordes, and Waste Materials: Metaphors of Immigrants and the Early Immigration Restriction Debate in the United States Metaphor and Symbol, 18:1, 33-47.
• RunRepeat 2020. Racial Bias in Football Commentary. 2020.

NLP, propaganda and misinformation

Overview and introductory articles (not available for presentations)

• Allcott, Hunt, and Matthew Gentzkow. 2016. "Social media and fake news in the 2016 election." Journal of economic perspectives 31, no. 2 (2017): 211-36.
• J. Scott Brennen, Felix Simon, Philip N. Howard, and Rasmus Kleis Nielsen. 2020. Types, sources, and claims of COVID-19 misinformation
• Milano, Silvia, Mariarosaria Taddeo, and Luciano Floridi. “Recommender systems and their ethical challenges.” Available at SSRN 3378581 (2019). https://link.springer.com/article/10.1007/s00146-020-00950-y
• Karishma Sharma, Feng Qian, He Jiang, Natali Ruchansky, Ming Zhang, and Yan Liu. 2019. Combating fake news: A survey on identification and mitigation techniques.
• Susser, Daniel, Beate Roessler, and Helen Nissenbaum. 2019. "Technology, autonomy, and manipulation." Internet Policy Review 8, no. 2 (2019).
• Vosoughi, Soroush, Deb Roy, and Sinan Aral. "The spread of true and false news online." Science 359, no. 6380 (2018): 1146-1151.
• Xinyi Zhou and Reza Zafarani. 2018. Fake news: A survey of research, detection methods, and opportunities.

Fake news detection

• Schuster, Tal, Darsh J. Shah, Yun Jie Serene Yeo, Daniel Filizzola, Enrico Santus, and Regina Barzilay. 2019. “Towards debiasing fact verification models.” EMNLP 2019.
• Schuster, Tal, Roei Schuster, Darsh J. Shah, and Regina Barzilay. 2020. The Limitations of Stylometry for Detecting Machine-Generated Fake News. Computational Linguistics.
• Shaden Shaar, Giovanni Da San Martino, Nikolay Babulkov, Preslav Nakov. 2020. That is a Known Lie: Detecting Previously Fact-Checked Claims. ACL 2020.
• Thorne, James, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2019. “Evaluating adversarial attacks against multiple fact verification systems.” EMNLP-IJCNLP, pp. 2937-2946.
• Zellers, Rowan, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. “Defending against neural fake news.” In Advances in Neural Information Processing Systems, pp. 9051-9062. 2019.

Corpora and studies on propaganda and misinformation

• Fatma Arslan, Naeemul Hassan, Chengkai Li, Mark Tremayne. 2020. A Benchmark Dataset of Check-worthy Factual Claims. Accepted to ICWSM 2020
• Atanas Atanasov, Gianmarco De Francisci Morales, Preslav Nakov. 2019. Predicting the Role of Political Trolls in Social Media. CoNLL 2019.
• Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle Augenstein. 2020. Generating Fact Checking Explanations. ACL 2020.
• Augenstein, Isabelle, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen. 2019. “MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims.” EMNLP-IJCNLP 2019.
• Adam Badawy, Kristina Lerman, and Emilio Ferrara. Who falls for online political manipulation? The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, pages 162–168, 2019.
• Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeño, Rostislav Petrov, Preslav Nakov. 2019. Fine-Grained Analysis of Propaganda in News Articles, EMNLP 2019.
• Anjalie Field, Doron Kliger, Shuly Wintner, Jennifer Pan, Dan Jurafsky, and Yulia Tsvetkov. 2018. Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies. EMNLP 2018
• Sharma, Karishma, Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla, Aastha Dua, and Yan Liu. 2020. Coronavirus on social media: Analyzing misinformation in Twitter conversations. arXiv
• Luceri, Luca, Ashok Deb, Adam Badawy, and Emilio Ferrara. “Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior.” arXiv preprint arXiv:1902.02765 (2019).
• Kai Nakamura, Sharon Levy, William Yang Wang. 2019. "Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection. "
• Rashkin, Hannah, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. “Truth of varying shades: Analyzing language in fake news and political fact-checking.” EMNLP 2017.
• Thorne, James, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. "FEVER: a Large-scale Dataset for Fact Extraction and VERification." NAACL 2018
• Toney, A., Pandey, A., Guo, W., Broniatowski, D., & Caliskan, A. (2021). Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter. In 2021 IEEE 15th International Conference on Semantic Computing (ICSC) (pp. 82-83). IEEE. https://arxiv.org/pdf/2004.08726.pdf
• Autumn Toney, Akshat Pandey, Wei Guo, David Broniatowski, Aylin Caliskan. 2020 Pro-Russian Biases in Anti-Chinese Tweets about the Novel Coronavirus
• Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Darrin Eide, Kathryn Funk, Rodney Kinney, Ziyang Liu, William Merrill, Paul Mooney, Dewey Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, Alex D. Wade, Kuansan Wang, Chris Wilhelm, Boya Xie, Douglas Raymond, Daniel S. Weld, Oren Etzioni, Sebastian Kohlmeier. 2020. CORD-19: The Covid-19 Open Research Dataset.
• Wang, William Yang. 2017. “Liar, liar pants on fire”: A new benchmark dataset for fake news detection. ACL 2017.
• Caleb Ziems, Bing He, Sandeep Soni, Srijan Kumar. 2020. Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 Crisis

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

• David A Broniatowski, Amelia M Jamison, SiHua Qi, Lulwah AlKulaib, Tao Chen, Adrian Benton, Sandra C Quinn, Mark Dredze. Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate. American Journal of Public Health (AJPH), 2018;108(10):1378-1384.
• Friggeri, Adrien, Lada Adamic, Dean Eckles, and Justin Cheng. 2014. "Rumor cascades." In Eighth International AAAI Conference on Weblogs and Social Media. 2014.
• Neil F. Johnson, Nicolas Velásquez, Nicholas Johnson Restrepo, Rhys Leahy, Nicholas Gabriel, Sara El Oud, Minzhang Zheng, Pedro Manrique, Stefan Wuchty and Yonatan Lupu. 2020. The online competition between pro- and anti-vaccination views. Nature.
• Wilson, Tom, and Kate Starbird. 2020. Cross-platform disinformation campaigns: lessons learned and next steps. Harvard Kennedy School Misinformation Review 1, no. 1 (2020).

NLP and health

Overview and introductory articles (not available for presentations)

• Benton, A., Coppersmith, G., & Dredze, M. (2017, April). Ethical research protocols for social media health research. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (pp. 94-102). https://aclanthology.org/W17-1612.pdf
• Šuster, S., Tulkens, S., & Daelemans, W. (2017). A short review of ethical challenges in clinical natural language processing. arXiv preprint arXiv:1703.10090. https://arxiv.org/pdf/1703.10090.pdf

Core reading list

• Afshar, M., Phillips, A., Karnik, N., Mueller, J., To, D., Gonzalez, R., … & Dligach, D. (2019). Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation. Journal of the American Medical Informatics Association, 26(3), 254-261.
• Althoff, T., Clark, K., & Leskovec, J. (2016). Large-scale analysis of counseling conversations: An application of natural language processing to mental health. Transactions of the Association for Computational Linguistics, 4, 463-476. https://aclanthology.org/Q16-1033.pdf
• Chary, M., Genes, N., Giraud-Carrier, C., Hanson, C., Nelson, L. S., & Manini, A. F. (2017). Epidemiology from tweets: estimating misuse of prescription opioids in the USA from social media. Journal of Medical Toxicology, 13(4), 278-286.
• Cohan, A., Desmet, B., Yates, A., Soldaini, L., MacAvaney, S., & Goharian, N. (2018). SMHD: a large-scale resource for exploring online language usage for multiple mental health conditions. Proceedings of the 27th International Conference on Computational Linguistics. https://aclanthology.org/C18-1126.pdf
• Coppersmith, G., Dredze, M., & Harman, C. (2014, June). Quantifying mental health signals in Twitter. In Proceedings of the workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality (pp. 51-60). https://aclanthology.org/W14-3207.pdf
• Coppersmith, G., Dredze, M., Harman, C., & Hollingshead, K. (2015). From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. In Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality (pp. 1-10). https://aclanthology.org/W15-1201.pdf
• Dligach, D., Afshar, M., & Miller, T. (2019). Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse. Journal of the American Medical Informatics Association, 26(11), 1272-1278.
• Hwang, J. D., & Hollingshead, K. (2016, June). Crazy mad nutters: the language of mental health. In Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology (pp. 52-62). https://aclanthology.org/W16-0306.pdf
• Jagannatha, A. N., & Yu, H. (2016, June). Bidirectional RNN for medical event detection in electronic health records. In Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting (Vol. 2016, p. 473). NIH Public Access. https://aclanthology.org/N16-1056.pdf
• Gkotsis, G., Oellrich, A., Hubbard, T., Dobson, R., Liakata, M., Velupillai, S., & Dutta, R. (2016, June). The language of mental health problems in social media. In Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology (pp. 63-73). https://aclanthology.org/W16-0307.pdf
• Sharma, A., Miner, A. S., Atkins, D. C., & Althoff, T. (2020). A computational approach to understanding empathy expressed in text-based mental health support. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://aclanthology.org/2020.emnlp-main.425.pdf
• Thompson, H. M., Sharma, B., Bhalla, S., Boley, R., McCluskey, C., Dligach, D., … & Afshar, M. (2021). Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups. Journal of the American Medical Informatics Association.