1. Allen, R., & Masters, D. (2020). Regulating for an equal AI: A new role for equality bodies Meeting the new challenges to equality and non-discrimination from increased digitisation and the use of Artificial Intelligence. Equinet. https://equineteurope.org/wp-content/uploads/2020/06/ai_report_digital.pdf.
2. Ahmed, S., & Swan, E. (2006). Doing Diversity. Policy Futures in Education], 4(2), 96-100. doi: 10.2304/pfie.2006.4.2.96. aaa(000) Avila, R., Brandusescu, A., Ortiz, J., & Thakur, T. (2018). Artificial Intelligence: open questions about gender inclusion. http:// webfoundation.org/docs/2018/06/AI-Gender.pdf. aaa(000) Berg, A.-J., & Lie, M. (1995). Feminism and Constructivism: Do Artifacts Have Gender? Science, Technology, & Human Values, 20(3), 332-351. doi: 10.1177/016224399502000304. aaa(000) Bimber, B. (2000). Measuring the gender gap on the Internet. Social Science Quarterly, 81(3), 868-876. aaa(000) 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. 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain. https://papers.nips.cc/paper/2016/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf. aaa(000) Bowen, G. (2009). Document Analysis as Qualitative Research Method. Qualitative Research Journal, 9(2), 27-40. doi: 10.3316/QRJ0902027. aaa(000) Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 1-15.
3. Semantics derived automatically from language corpora contain human-like biases;Caliskan;Science,2017
4. Camilli, G. (2005). Test fairness. In R. Brennan (Ed.), Educational Measurement. American Council on Education/Praeger, pp. 221-256.
5. ‘Artificial Intelligence and the “Good Society”: the US, EU, and UK approach’;Cath;Science and Engineering Ethics,2018