Lecture 11: October 26
slides: ODP | PDF (screen) | PDF (print)
Full recording: part 1 and part 2.
Mandatory reading:
- Hovy, D., & Spruit, S. L. (2016). The social impact of natural language processing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 591-598).
Bolukbasi, T., Chang, K. W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in neural information processing systems (pp. 4349-4357). NB: you can skip the details of the experimental design and evaluation results.
- Ziyuan Zhong, "A tutorial on Fairness in Machine Learning", Towards Data science. NB: you can skip Section 5 of the text.
- Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144). NB: you can skip Section 4 of the paper, as well as the details of the experimental design and evaluation results.
- Chapter 2 of Domingo-Ferrer, J., Sánchez, D., & Soria-Comas, J. (2016). Database anonymization: privacy models, data utility, and microaggregation-based inter-model connections. Synthesis Lectures on Information Security, Privacy, & Trust, 8(1), 1-136. NB: You can skip the technical details on measuring information loss.
Optional reading:
- Blackburn, S. (2002). Being good: A short introduction to ethics. OUP Oxford.
- Poppy Noor (2018), Wikipedia biases, The Guardian.
- Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., & Goel, S. (2020). Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences, 117(14), 7684-7689.
- Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The state and fate of linguistic diversity and inclusion in the NLP world. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp 6282–6293).
- Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018). Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences , 115 (16), E3635-E3644.
- Crawford & T. Paglen (2019) "Excavating AI: The politics of images in machine learning training sets".
- Vanmassenhove, E., Hardmeier, C., & Way, A. (2018). Getting Gender Right in Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3003-3008).
- Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016). On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236.
- De Angelia, A., & Brahnamb, S. (2008). I hate you! Disinhibition with virtual partners. Interacting with Computers, 20, 302-310.
- P. Harish (2019), Chatbots and abuse: A growing concern. Medium.
- Fort, K., Adda, G., & Cohen, K. B. (2011). Amazon mechanical turk: Gold mine or coal mine? Computational Linguistics, 37(2), 413-420.
- Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645-3650).
- Zhou, X., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR), 53(5), 1-40.
- Jayshree Pandya (2019), The Dual-Use Dilemma Of Artificial Intelligence, Forbes.
- Wagner, A. R., Borenstein, J., & Howard, A. (2018). Overtrust in the robotic age. Communications of the ACM, 61(9), 22-24.
- Williams, M. L., Burnap, P., & Sloan, L. (2017). 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.
Exercise session 10: October 27
- Review of core ethical concepts.
- Help with obligatory assignment 3.