Hila Fridman, Rinat Arviv Elyashiv, Miri Shonfeld E65 References Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT*), 77-91. https://proceedings.mlr.press/v81/buolamwini18a.html Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183-186. https://doi.org/10.1126/science.aal4230 Gay, G. (2018). Culturally responsive teaching: Theory, research, and practice (3rd ed.). Teachers College Press. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 115. https://doi.org/10.1145/3457607 Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59-74. https://doi.org/10.1177/1094670514539730 Parrillo, V. N., & Donoghue, C. (2005). Updating the Bogardus social distance studies: A new national survey. The Social Science Journal, 42(2), 257-271. https://doi.org/10.1016/j.soscij.2005.03.011 Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009 Siwatu, K. O. (2007). Preservice teachers' culturally responsive teaching self-efficacy and outcome expectancy beliefs. Teaching and Teacher Education, 23(7), 1086-1101. https://doi.org/10.1016/j.tate.2006.04.038 Teo, T. (2011). Factors influencing teachers' intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. https://doi.org/10.1016/j.compedu.2011.06.008
RkJQdWJsaXNoZXIy Mjk0MjAwOQ==