Credit: Anna Logue
Text Analytics Seminar (HWS 2018: Ethics in NLP)
This semester we will look at ethical challenges in Natural Language Processing.
Organization
- This seminar is organized by Anne Lauscher, Federico Nanni and Simone Paolo Ponzetto.
- The course is restricted to 12 participants.
- Available for master students
Schedule
The seminar will take place in C1.01, dates TBA.
Goals
In this seminar, you will have to meet the following requirements:
- Read, understand, and explore scientific literature related to one of the topics that can be found below
- Learn how your topic is reflected by media and society
- Discuss and debate with the other participants
- Summarize your topic in a concise report
- Give a presentation on your topic focusing on two scientific publications (25 minutes presentation + 20 minutes questions and discussion)
Prerequisites
- Successful completion of IE 661 “Text Analytics” or IE 663 “Web Search and Information Retrieval”.
- Interest in discussing the topic with your fellow students
Topics + Literature
The seminar will touch on the following topics. Please use the literature below as a starting point and also check out the proceedings of the Workshop on Ethics in NLP (e.g., 2017).*
exclusion/
discrimination/ bias - Park, J.H. & Shin, J. & Fung, P. (2018). Reducing Gender Bias in Abusive Language Detection. arXiv.
- Angwin, J., & Larson, J. (30 Dec 2016). Bias in criminal risk scores is mathematically inevitable, researchers say. ProPublica.
- boyd, d. (2015). What world are we building? (Everett C Parker Lecture. Washington, DC, 20 October)
- Brennan, M. (2015). Can computers be racist? big data, inequality, and discrimination. (online; Ford Foundation)
- Clark, J. (23 Jun 2016). Artificial intelligence has a `sea of dudes' problem. Bloomberg Technology.
- Crawford, K. (1 Apr 2013). The hidden biases in big data. Harvard Business Review.
- Daumé III, H. (8 Nov 2016). Bias in ML, and teaching AI. (Blog post, accessed 1/
17/17) - Emspak, J. (29 Dec 2016). How a machine learns prejudice: Artificial intelligence picks up bias from human creators--not from hard, cold logic. Scientific American.
- Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3), 330–347.
- Guynn, J. (10 Jun 2016). `Three black teenagers' Google search sparks outrage. USA Today.
- Hardt, M. (26 Sep 2014). How big data is unfair: Understanding sources of unfairness in data driven decision making. Medium.
- Jacob. (8 May 2016). Deep learning racial bias: The avenue Q theory of ubiquitous racism. Medium.
- Larson, J., Angwin, J., & Parris Jr., T. (19 Oct 2016). Breaking the black box: How machines learn to be racist. ProPublica.
- Morrison, L. (9 Jan 2017). Speech analysis could now land you a promotion. BBC capital.
- Rao, D. (n.d.). Fairness in machine learning. (slides)
- Sweeney, L. (1 May 2013). Discrimination in online ad delivery. Communications of the ACM, 56 (5), 44–54.
- Zliobaite, I. (2015). On the relation between accuracy and fairness in binary classification. CoRR, abs/
1505.05723.
democracy and the language of manipulation
- Cutler, A. and Kulis, B. (2018). Inferring Human Traits From Facebook Statuses. arXiv.
- Yao, M. (n.d.). Can bots manipulate public opinion? (Web page, accessed 12/
29/16) - www.bloomberg.com/features/2016-how-to-hack-an-election/ (Web page, accessed 12/
29/16) - https://www.nytimes.com/interactive/2018/05/14/technology/facebook-ads-congress.html (News Article, accessed 09/
10/18) - https://www.nytimes.com/2017/11/01/us/politics/russia-2016-election-facebook.html (News Article, accessed 09/
10/18) - https://www.politico.eu/article/cambridge-analytica-chris-wylie-brexit-trump-britain-data-protection-privacy-facebook/ (News Article, accessed 09/
10/18)
privacy/
intellectual property - Abadi, M., Chu, A., Goodfellow, I., Brendan McMahan, H., Mironov, I., Talwar, K., et al. (2016). Deep Learning with Differential Privacy. ArXiv e-prints.
- Amazon.com. 2017. Memorandum of Law in Support of Amazon's Motion to Quash Search Warrant
- Brant, T. (27 Dec 2016). Amazon Alexa data wanted in murder investigation. PC Mag.
- Friedman, B., Kahn Jr, P. H., Hagman, J., Severson, R. L., & Gill, B. (2006). The watcher and the watched: Social judgments about privacy in a public place. Human-Computer Interaction, 21(2), 235–272.
- Golbeck, J., & Mauriello, M. L. (2016). User perception of facebook app data access: A comparison of methods and privacy concerns. Future Internet, 8(2), 9.
- Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of “personally identifiable information”. Communications of the ACM, 53 (6), 24–26.
- Nissenbaum, H. (2009). Privacy in context: Technology, policy, and the integrity of social life. Stanford: Stanford University Press.
- Solove, D. J. (2007). 'I've got nothing to hide' and other misunderstandings of privacy. San Diego Law Review, 44 (4), 745–772.
- Steel, E., & Angwin, J. (4 Aug 2010). On the Web's cutting edge, anonymity in name only. The Wall Street Journal.
- Tene, O., & Polonetsky, J. (2012). Big data for all: Privacy and user control in the age of analytics. Northwestern Journal of Technology and Intellectual Property, 11(45), 239–273.
- Vitak, J., Shilton, K., & Ashktorab, Z. (2016). 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).
- usableprivacy.org/publications (compiled list of NLP publications related to the topic)
chat bots
- Fessler, Leah. (22 Feb 2017). SIRI, DEFINE PATRIARCHY: We tested bots like Siri and Alexa to see who would stand up to sexual harassment. Quartz.
- Fung, P. (3 Dec 2015). Can robots slay sexism? World Economic Forum.
- Mott, N. (8 Jun 2016). Why you should think twice before spilling your guts to a chatbot. Passcode.
- Paolino, J. (4 Jan 2017). Google home vs Alexa: Two simple user experience design gestures that delighted a female user. Medium.
- Seaman Cook, J. (8 Apr 2016). From Siri to sexbots: Female AI reinforces a toxic desire for passive, agreeable and easily dominated women. Salon.
- Twitter. (7 Apr 2016). Automation rules and best practices. (Web page, accessed 12/
29/16) - Yao, M. (n.d.). Can bots manipulate public opinion? (Web page, accessed 12/
29/16)
word embeddings and language behavior
- Bolukbasi, T., Chang, K., Zou, J. Y., Saligrama, V., & Kalai, A. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. CoRR, abs/
1607.06520. - Caliskan-Islam, A., Bryson, J., & Narayanan, A. (2016). A story of discrimination and unfairness. (Talk presented at HotPETS 2016, Video of presentation)
- Daumé III, H. (2016). Language bias and black sheep. (Blog post, accessed 12/
29/16) - Herbelot, A., Redecker, E. von, & Müller, J. (2012, April). Distributional techniques for philosophical enquiry. In Proceedings of the 6th workshop on language technology for cultural heritage, social sciences, and humanities (pp. 45–54). Avignon, France: Association for Computational Linguistics.
- Schmidt, B. (2015). Rejecting the gender binary: A vector-space operation. (Blog post, accessed 12/
29/16)
- Bolukbasi, T., Chang, K., Zou, J. Y., Saligrama, V., & Kalai, A. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. CoRR, abs/
NLP techniques and applications for addressing ethical issues
- Fokkens, A. (2016). Reading between the lines. (Slides presented at Language Analysis Portal Launch event, University of Oslo, Sept 2016)
- Gershgorn, D. (27 Feb 2017). NOT THERE YET: Alphabet's hate-fighting AI doesn't understand hate yet. Quartz.
- Google.com. (2017). The women missing from the silver screen and the technology used to find them. Blog post, accessed 1 March 2017.
- Greenberg, A. (2016). Inside Google'S Internet Justice League and Its AI-Powered War on Trolls. Wired.
- Kellion, L. (1 Mar 2017) Facebook artificial intelligence spots suicidal users. BBC News.
- Munger, K. (2016). Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior, 1–21.
- Munger, K. (17 Nov 2016). This researcher programmed bots to fight racism on twitter. It worked. Washington Post.
- Murgia, M. (23 Feb 2017). Google launches robo-tool to flag hate speech online. Financial Times.
- The times is partnering with jigsaw to expand comment capabilities. (20 Sep 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: CEO Kieran Snyder's posts on medium.com; Recording of Kieran Snyder's NLP Meetup talk from 15 Aug 2016
* The literature is mostly taken from http://faculty.washington.edu/ebender/2017_575/.