Chair: Ksenija Dumicic, University of Zagreb, Croatia, Chair of ISI Committee on Women in Statistics
Jackie Carter, The University of Manchester, School of Social Sciences, United Kingdom “Getting more women into data science careers: reflecting on a decade of a data fellows experiential learning programme”
Blagica Novkovska, University of Skopje, Faculty of Economics, Skopje, The Republic of North Macedonia “Role of the data science in gender equality studies: towards a new equitable and prosperous world” b.novkovska@utms.edu.mk (blagica@novkovski.com)
Haoyi Chen, Coordinator Inter-Secretariat Working Group on Household Surveys – UNSD “Experiences of an official sampling statisticians collaborating with UN-Women”
Paola Vicard, University Roma Tre “Bayesian Network as a novel data science tool to improve, measuring and predicting gender equality at sub-national level” paola.vicard@uniroma3.it
Discussant: Fulvia Mecatti, University Miano Bicocca
Besides being a fundamental human right and  a democratic value, gender equality is a prominent goal in the UN Agenda 2030 for sustainable development  https://sdgs.un.org/goals/goal5, a constitutional
principle of the European Union (Article 2 and Article 3(3) of The treaty on European Union) and a recognized driver of social development and economic growth (https://eige.europa.eu/newsroom/economic-benefits-gender-equality?language%20content%20entity=en#toc-study-s-publications)
According to the UN (the lack of) gender equality is a global issue, that concerns half of the world’s population, and limits half of its potential as a result www.un.org/en/global-issues/gender-equality. Therefore, a paramount need poses, for ever-improved statistical methods and tools to tackle gender gaps and to assess gender equality within diverse contexts: national, local, sub-group or thematic. The proposed IPS is intended to explore novel data science methods, applications and discussion from various approaches and perspectives. 
This IPS is proposed by the ISI Committee on Women in Statistics and by the ISI Special Interest Group on Data Science.