The Gender and Responsible Artificial Intelligence Network (GRAIN) is organising a virtual debate on Thursday 30 May 2024 from 3pm to 5pm GMT on the Zoom platform on ‘How to get more women involved in STEM‘.
The virtual meeting will provide an opportunity to exchange views with network members and other stakeholders in STEM (Science, Technology, Engineering and Mathematics) on strategies and opportunities to promote women’s involvement in STEM, and to discuss ways of strengthening their inclusion and active participation.
Specifically, it will assess the educational and career opportunities in STEM fields for women in Africa; identify successful initiatives and programmes that have attracted women to STEM and understand the key factors behind their success; explore the specific barriers and challenges that women face in their educational and career pathways within STEM; and formulate strategic recommendations for policymakers, the business sector and civil society to support and encourage women to pursue careers in STEM.
Moderated by Dr Ndeye Fatou CISSE, a gender expert at IPAR, the virtual event will bring together Ms Minata SARR, PhD in digital law, lecturer and researcher; ; Ms Maimouna Leye Diakhate, President of SENUM (Synergy for Digital and Media Education); ; Joyce Nabende : Lecturer in Computer Science at Makere University, Kenya; ; Dr Juliet Moso : Lecturer at Dedan Kimathi University of Technology; and Marwa Cheikh-Youssef, President of HACK&PITCH, Morocco.
Using a brainstorming format involving all participants to encourage open and collaborative thinking, the debate will include STEM experts, representatives of women’s groups and others, with a view to fostering an inclusive and participatory approach to ensure that all voices are heard and that the discussions are enriching and constructive.
Expected outcomes include the exchange of information and best practice to promote women’s participation in STEM; increased awareness of the importance of women’s inclusion in STEM and the actions needed to increase their participation and success in these fields.
Not so long ago, people lived and went about their daily lives in close-knit communities. Every shopkeeper knew their customers personally and could...
This Machine Learning Glossary aims to provide a brief introduction to the most important machine learning terms – both for commercial and...
FR 
EN 
