LING 575 — Ethical Considerations in NLP
A quick pitch for my upcoming WIN 19 course, covering some of the topics from this course.
N.B.: If you are a student enrolled in this course, please refer to the course page on Canvas. This page is for public-facing and archival purposes and may not be up-to-date for coursework.
- Lecture: Wednesdays, 3:30-5:50 in MGH 288 and online
- Ryan Georgi
- Office Hours: Wednesdays 12:30-2:30.
- Office: GUG 418-D
The goal of this course is to better understand the ethical considerations that arise in the deployment of NLP technology, including (but not limited to) considerations of demographic misrepresentation, bias confirmation, and privacy. We will start with foundations in ethics, and then move to the current and growing research literature on ethics in NLP and allied fields, before considering specific NLP tasks, data sets and training methodologies through the lens of the ethical considerations identified. Course projects are expected to take the form of a term paper analyzing some particular NLP task or data set in terms of the concepts developed through the quarter and looking forward to how ethical best practices could be developed for that task/data set.
In particular, I hope to find answers to the following guiding questions over the course of the term:
- What ethical considerations arise in the design and deployment of NLP technologies?
- Which of these are specific to NLP (as opposed to AI or technology more generally?)
- What best practices can/should NLP developers deploy in light of the ethical concerns identified?
Note: To request academic accommodations due to a disability, please contact Disability Resources for Students , 448 Schmitz, 206-543-8924 (V/TTY). If you have a letter from DSR indicating that you have a disability which requires academic accommodations, please present the letter to the instructor so we can discuss the accommodations you might need in this class.
- KWLA paper (approx 7 pages) (15)
- Proposed NLP/ML ethics code critique (20)
- Participation in discussions (incl. Canvas) (15)
- Term project (50)
Schedule of Topics and Assignments (subject to change)
Why are we here? What do we hope to accomplish?
|Hovy and Spruit 2016 plus at least 2 other papers/articles listed under Overviews/Calls to Action (or just one, if you pick something particularly long)|
|4/2||KWLA papers: K & W due 11pm|
|4/4||Philosophical foundations||2 items from Philosophical Foundations, at least one of which comes from an author whose perspective varies greatly from your own life experience. Be prepared to discuss the following:
|4/11||Philosophical foundations (cont)|
|4/18||Exclusion/Discrimination/Bias||3–4 items from Exclusion/Discrimination/Bias, considering the following reading questions (not all of which are necessarily appropriate for all readings):
|4/25||Word Embeddings and Language Behavior as Ground Truth
|2 items from each of Word Embeddings and Language Behavior as Ground Truth and Chat bots, considering the following reading questions (not all of which are necessarily appropriate for all readings):
|5/2||Proposed code of ethics for ACL
Term project brainstorm
|5/7||Term paper proposals due|
|5/9||Value Sensitive Design||Read any two other papers from Value Sensitive Design. Reading questions:
In addition, for an NLP project you are interested in:
|5/14||Proposed NLP/ML ethics code critique due|
|5/16||Other Best Practices||Read at least three papers from Other Best Practices. Reading/discussion questions:
||Term paper outline due|
|5/23||Privacy||Read at least three papers from Privacy. At least one should be from a CS-type perspective and at least one from a non-CS scholarly perspective (social sciences or law). Reading/discussion questions:
|5/28||Term paper draft due|
|5/30||NLP Applications Addressing Ethical Issues||Choose three of the items under NLP Apps Addressing Ethical Issues below and be prepared to discuss the following reading questions:
|6/1||KWLA papers due
Comments on partner’s paper draft due
|6/6||Final papers due 11pm|
- Overviews/Calls to Action
- Philosophical Underpinnings
- Human Subjects & Social Media Research
- Word Embeddings and Language Behavior as Ground Truth
- Chat Bots
- Abusive Language Online
- NLP Applications
- Value Sensitive Design
- Proposals for Codes of Ethics
- Other Best Practices
- Other Resources
- Other Courses
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- Particularly, Chapters:
- 2: Users’ Views of Ethics in Social Media Research: Informed Consent, Anonymity, and Harm
- 5: Informed Consent in Qualitative Social Media Research
- 7: Ethical Challenges of Publishing and Sharing Social Media Research Data
- 8: The Ethics of Using Social Media Data in Research: A New Framework
- Particularly, Chapters:
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- Larson, J., Angwin, J., & Parris Jr., T. (Oct 19, 2016). Breaking the black box: How machines learn to be racist. ProPublica.
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- Sweeney, L. (May 1, 2013). Discrimination in online ad delivery. Communications of the ACM, 56 (5), 44-54.
- Tatman, R., 2017: Gender and Dialect Bias in YouTube’s Automatic Captions. Proceedings of the First Workshop on Ethics in Natural Language Processing.
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- Caliskan-Islam, A., Bryson, J., & Narayanan, A. (2016). A story of discrimination and unfairness. (Talk presented at HotPETS 2016)
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- Mott, N. (Jun 8, 2016). Why you should think twice before spilling your guts to a chatbot. Passcode.
- Paolino, J. (Jan 4, 2017). Google home vs Alexa: Two simple user experience design gestures that delighted a female user. Medium.
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- Twitter. (Apr 7, 2016). Automation rules and best practices. (Web page, accessed 12/29/16)
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- Clarke, I., & Grieve, J. (2017). Dimensions of Abusive Language on Twitter. Presented at the First Workshop on Abusive Language Online. Retrieved from https://drive.google.com/file/d/0B4xDAGbwZJjQSlJzQWZscjhsa0E/view?usp=embed_facebook
- Gambäck, B., & Sikdar, U. K. (2017). Using Convolutional Neural Networks to Classify Hate-Speech. Presented at the Proceedings of the First Workshop on Abusive Language Online.
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- Samghabadi, N. S., Maharjan, S., Sprague, A., Diaz-Sprague, R., & Solorio, T. (2017). Detecting Nastiness in Social Media. Presented at the First Workshop on Abusive Language Online, Vancouver, Canada.
- Waseem, Z., & Hovy, D. (2016). Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter (pp. 88–93). Presented at the Proceedings of NAACL-HLT 2016, San Diego, California.
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- The times is partnering with jigsaw to expand comment capabilities. (Sep 20, 2016). The New York Times.
- Fake News Challenge
- Jigsaw Challenges
- Perspective (from Jigsaw)
- But see: Hosseini, H, S. Kannan, B. Zhang and R. Poovendran. 2017. Deceiving Google’s Perspective API Built for Detecting Toxic Comments. ArXiv.
- Textio See also:
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- Callison-Burch, C. (2016). Ethics of crowdsourcing. (Slides from Crowdsoucing and Human Computation, accessed online 12/30/16)
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- The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems. (2016). Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems (AI/AS) (Version 1 — For Public Discussion).
- Etlinger, S., & Groopman, J. (2015). The trust imperative: A framework for ethical data use.
- Daumé III, H. (Dec 12, 2016). Should the NLP and ML Communities have a Code of Ethics? (Blog post, accessed 12/30/16)
- Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. CoRR, abs/1606.06565.
- Markham, A. (2012). Fabrication as ethical practice: Qualitative inquiry in ambiguous Internet contexts. Information, Communication & Society, 15(3), 334-353.
- Ratto, M. (2011). Critical making: Conceptual and material studies in technology and social life. The Information Society, 27 (4), 252-260.
- Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and benefcial artifcial intelligence. AI Magainze.
- Shilton, K., & Anderson, S. (2016). Blended, not bossy: Ethics roles, responsibilities and expertise in design. Interacting with Computers.
- Shilton, K., & Sayles, S. (2016). “We aren’t all going to be on the same page about ethics”: Ethical practices and challenges in research on digital and social media. In 2016 49th Hawaii international conference on system sciences (HICSS) (pp. 1909-1918).
- Ethics in Natural Language Processing (2017) (2018) at NAACL 2018, June 5th, New Orleans, Louisiana, USA.
- 3rd International Workshop on AI, Ethics and Society 4th or 5th February 2017 San Francisco, USA
- PDDM16 The 1st IEEE ICDM International Workshop on Privacy and Discrimination in Data Mining December 12, 2016 – Barcelona
- Machine Learning and the Law NIPS Symposium 8 December, 2016 Barcelona, Spain
- AAAI Fall Symposium on Privacy and Language Technologies, November 2016
- Workshop on Data and Algorithmic Transparency (DAT’16) November 19, 2016, New York University Law School
- WSDM 2016 Workshop on the Ethics of Online Experimentation, February 22, 2016 San Francisco, California
- ETHI-CA2 2016: ETHics In Corpus Collection, Annotation and Application LREC 2016, Protoroz, Slovenia.
- Fairness, Accountability, and Transparency in Machine Learning, 2014, 2015, 2016
- ETeRNAL – Ethique et TRaitemeNt Automatique des Langues June 22, 2015, Caen
- Éthique et Traitement Automatique des Langues, Journée d’étude de l’ATALA Paris, France, November 2014
- Critical Algorithm Stuides
- FATML resources page
- The Responsible Conduct of Computational Modeling and Research NSF funded project
- A Course on Fairness, Accountability and Transparency in Machine Learning (Suresh Venkatasubramanian)
- Ethics for the Information Age (Michael Quinn)
- Another list of courses from Thomast Morgan Jr. at ETSU
- The Dark Side of NLP: Gefahren automatischer Sprachverarbeitung (Michael Strube)