Lecture November 3:
Ethics for NLP (part 1)
Slides: PPTX | PDF
Recordings
Mandatory reading
- Ziyuan Zhong, "A tutorial on Fairness in Machine Learning", Towards Data science. NB: you can skip Section 5 of the text.
Optional reading:
- 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).
Lab-session, Tuesday, November 8 at Sed
Practical help with the third obligatory assignment.