It’s no secret that the careers of the future have become a catch-up game. With new tech emerging at a faster-than-human speed, the world needs to unite together to ensure that equal opportunities exist for underrepresented communities. With this in mind, B.O.T & UNICEF Lebanon are proud to launch “AI Training for women” under UNICEF’s Gender Equality program.
Funded by UNICEF, the program will allow 75 young women and girls to develop careers in Data Annotation by learning the basics in theory and then applying it to generate sustainable incomes.
The project is multidimensional and aims at achieving the following objectives:
- Support 75 young women and girls from low-income communities get in-depth training in Data Annotation
- Provide real life work opportunities for the program trainees and generate sustainable incomes for them
- Raise awareness about the importance of Data Annotation in the Arab Region
- Provide free-of-charge Data Annotation services to startups around the world enabling them to enhance their AI Products
Also within the program sphere, B.O.T launched a competition among Startups in the region who work in the AI realm. This competition will provide the startups with a chance to win free Data Annotation services by B.O.T and train their AI models on high-quality datasets.
75 young women will be digitally upskilled and trained to perform Data Annotation tasks. Through this training, they will learn about AI and Machine Learning through curriculums by IBM, Microsoft, and real-life experiences that will help them launch a career in tech.
Training Curriculum:
The training curriculum consists of a total of 11 sessions divided into 2 modules – Theoretical and Practical. Supplementary activities such as Workshops and “Day in AI” are designed to bring trainees the real-life work environment of an AI company. Workshops are aimed to help trainees discover future training opportunities and better define their career path in Tech. The duration of this training is 5-6 weeks.
The Theoretical module provides a comprehensive introduction into the field of Artificial Intelligence and its subfields – Computer Vision (CV) and Natural Language Processing (NLP) as well as Data Science. Upon completion of the module, the trainees will be able to understand basic concepts and use cases. They will also have knowledge of basic manipulations with data, data annotation and its application in the AI models, as well as privacy, quality and security concerns related to AI.
The Practical module provides the trainees with practical skills required to perform data annotation tasks for CV and NLP. Upon completion of this module, trainees will be able to differentiate between different types of data annotation tasks and will perform these tasks using various tools. They will also have awareness of most common use cases and errors associated with data labeling and their implications.
Upon completion of this course, the trainees receive certificates from DOT Lebanon.
Trainees Profiles:
During the program implementation period, 85 young women enrolled and were split into 3 cohorts.
These young women come from diverse Educational backgrounds; 60% hold a Bachelor’s degree, 26% hold a Master’s degree and 13% have a High School diploma or have pursued a Vocational Training.
In terms of Specializations, 35% of the ladies have a background in Computer Science & IT, 10% studied Engineering & Sciences, 10% are in the Biomedical field, another 10% specialize in Linguistics and finally, 35% come from Other specializations.
Pilot Projects:
On another note, these ladies are not only being trained. They are also getting the chance to work on pilot projects which will expose them even more to the world of AI while allowing them to secure a source of income.
B.O.T is always on the lookout for ways to make this world a better place. The team is very happy to see these ladies learn and grow by acquiring new skills – especially within the AI world – while securing a source of income. A huge thanks to UNICEF, without whom this initiative would not be possible.
