待補

待補

各授課教師各自評量成績，最後再以六位授課老師之平均成績為學生總成績。 1. 數位人文概論 (圖資系陳光華老師):專題小論文80%；課程參與20%。 2. 數位博物館 (圖資系林維真老師):課堂討論40%；學習反思心得報告60%。 3. 語言表現的科學視角(語言所邱振豪老師)：作業80%；出席20% 4. 大型語言模型與人文研究(語言所謝舒凱老師)：作業70%；出席30%。 5. 科學思想與人文(哲學系陳樂知老師)：平時參與及出席20%；作業報告80%。 6. AI與倫理(哲學系史德凡老師)：平時參與及出席20%；作業報告80%。

1. Introduction of Digital Humanities (Prof. Kuang-hua Chen) • Drucker, J., Kim, D., & Salehian, I. (2013). Introduction to digital humanities: Concept, method, and tutorials for students and instructors. https://stacks.stanford.edu/file/druid:vw253bt6706/IntroductionToDigitalHumanities_Textbook.pdf • Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press. • Hartsell-Gundy, A., Braunstein, L., & Golomb, L. (2015) (Eds.). (2015). Digital humanities in the library: Challenges and opportunities for subject specialists. ACRL. http://www.ala.org/acrl/sites/ala.org.acrl/files/content/publications/booksanddigitalresources/digital/9780838987681_humanities_OA.pdf 2. Digital Museums (Prof. Weijane Lin) • Freeman, A., Becker, S. A., Cummins, M., McKelroy, E., Giesinger, C., & Yuhnke, B. (2016). NMC horizon report: 2016 museum edition. The New Media Consortium. • Price, S., Jewitt, C. & Moussouri, T. (2022). Supporting family scaffolding and collaboration through digital interactive tabletop exhibits. Visitor Studies, 25(1), 22–40. • Yoon, S. A., Elinich, K., Wang, J., Steinmeier, C., & Van Schooneveld, J. G. (2012). Learning impacts of a digital augmentation in a science museum. Visitor Studies, 15(2), 157–170. 3. The scientific view of linguistic expression (Prof. Chenhao Chiu) • McGurk, H., & MacDonald, J. (1976). Hearing lips and seeing voices. Nature, 264(5588), 746-748. • Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274(5294), 1926-1928. • Houde, J. F., & Jordan, M. I. (1998). Sensorimotor adaptation in speech production. Science, 279(5354), 1213-1216. • Tremblay, S., Shiller, D. M., & Ostry, D. J. (2003). Somatosensory basis of speech production. Nature, 423(6942), 866-869. • Gick, B., & Derrick, D. (2009). Aero-tactile integration in speech perception. Nature, 462(7272), 502-504. 4. Large Language Model and Humanities Research (Prof. Shu-Kai Hsieh)：No References 5. Scientific thinking and humanities (Prof. Lok-Chi Chan) (1) Week 1 • Kim, J. (2002). Mental causation. In B. McLaughlin, A. Beckermann, & S. Walter (Eds.), The Oxford handbook of philosophy of mind. Oxford University Press. • Ney, A. (2018). Physicalism, not scientism. In J. de Ridder, R. Peels & R. van Woudenberg (Eds.), Scientism: Prospects and problems. Oxford University Press. • Oppy, G. (2014). Reinventing philosophy of religion: An Opinionated introduction. Palgrave Macmillan. (2) Week 2 • Gennaro, R. (2014). 'Consciousness' (especially Section 4), Internet Encyclopedia of Philosophy. https://iep.utm.edu/consciousness/ • Blackmore, S. (2017). Consciousness: A very short introduction (2nd ed.). Oxford University Press. 6. AI and ethics (Prof. Shawn Standefer) • DeBrabander, F. (2020). Life after privacy: Reclaiming democracy in a surveillance society. Cambridge University Press. • Gabriel, I. (2020). Artificial intelligence, values, and alignment. Minds and Machines, 30(3), 411–437. • Gal, D. (2020). Perspectives and approaches in AI ethics: East Asia. In M. Dubber, F. Pasquale, & S. Das (Eds.), Oxford handbook of ethics of artificial intelligence (pp. 607–624). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190067397.013.39 • Kim, T.W., Hooker, J., & Donaldson, T. (2021). Taking principles seriously: A hybrid approach to value alignment. Journal of Artificial Intelligence Research, 70, 871–890. • Lechterman, T. M. (2022). The concept of accountability in AI ethics and governance. In J. Bullock, Y. C. Chen, J. Himmelreich, V. Hudson, M. Korinek, M. Young, & B. Zhang (Eds.), Oxford handbook of AI governance. Oxford University Press. • Lin, Y. T., Hung, T. W., & Huang, L. T. L. (2020). Engineering equity: How AI can help reduce the harm of implicit bias. Philosophy and Technology, 34(S1), 65–90. • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. • O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishers. • Ord, T. (2020). The precipice: Existential risk and the future of humanity. Bloomsbury. • Paez, A. (2019). The pragmatic turn in explainable artificial intelligence (XAI). Minds and Machines, 29(3), 441–459. • Wallach, W., & Allen, C. (2008). Moral machines: Teaching robots right from wrong. Oxford University Press. • Watson, D. S., & Floridi, L. (2020). The explanation game: A formal framework for interpretable machine learning. Synthese, 198 (10), 1–32.