The adoption of digital transformation was not expressed in building an African face shape classifier. In this paper, an approach is presented that uses k-means to classify African women images. African women rely on beauty standards recommendations, personal preference, or the newest trends in hairstyles to decide on the appropriate hairstyle for them. In this paper, an approach is presented that uses K-means clustering to classify African women's images. In order to identify potential facial clusters, Haarcascade is used for feature-based training, and K-means clustering is applied for image classification.

By raising awareness about the different pathways into AI and making it more accessible, we want to inspire participation from historically underrepresented groups so that together we can build a more equitable and ethical tech future. AI Ethics and Policy Researcher, Favour Borokini takes us through her career journey and shares what inspired her to join this space and how she landed her current role at Pollicy. She also talks about some of the most common barriers and challenges she tackles on a daily basis and how she deals with them as someone who comes from a non-technical background. She also shares her thoughts on diversity and the most practical tips to get started in this space especially if you're someone who comes from a non-technical background. You can listen to the podcast or read through their conversation below.

“Building the New Reality” Who We Are: We are Africa’s FIRST finance and technology talent accelerator for women! Yielding Accomplished African Women aims at erecting and cultivating the largest community of African female developers and financial analysts who are passionate about using STEM to revolutionize Africa and beyond. We are creating this online community for African women across the continent. Yaa W. is introducing Africa's FIRST Machine Learning conference for Young Women. Yielding Accomplished African Women (Yaa W.) presents “Solving the Algorithm: Women in Machine Learning Conference." According to the United Nations Development Program, 66% of sub-saharan African women work in informal labor markets and in the age of technology many of these jobs may be lost in the future due to automation. This fully funded conference will be an opportunity to equip African Women with the tools and skills needed to be leaders in this emerging field. Participants will enjoy a day immersive experience with: - Inspiring keynotes - Machine learning tutorials - Networking with Google employees - Community building exercises with other women in tech - Professional development training & More........ Final Deadline - December 12th 11:59PM GMT

Artificial intelligence took center stage as African female technology experts met at Women in Tech Week in Ghana to promote women's involvement in the field. When Lily Edinam Botsyoe was studying computer science at a university in Ghana, students wrote programming codes on a whiteboard because there were not enough computers. This made it difficult to apply the coding skills they were learning, she says, and the problem continues today. "We have students coming out of schools having the theoretical background -- which is very important because you can't actually appreciate something practical if you don't have the theory. But, the industry-ready skills is lacking because they didn't have the hands-on experience," Botsyoe said.

ACCRA - Artificial intelligence took center stage as African female technology experts met at Women in Tech Week in Ghana to promote women's involvement in the field. When Lily Edinam Botsyoe was studying computer science at a university in Ghana, students wrote programming codes on a whiteboard because there were not enough computers. This made it difficult to apply the coding skills they were learning, she says, and the problem continues today. "We have students coming out of schools having the theoretical background -- which is very important because you can't actually appreciate something practical if you don't have the theory. But, the industry-ready skills is lacking because they didn't have the hands-on experience," Botsyoe said.

Organizations are increasingly reliant on Machine Learning (ML) models to weigh in on decisions to hire, grant loans, sentence criminals, and release prisoners on parole. While it may seem that limiting the role of humans in such decisions would limit subjective biases, these ML models learn from data that are, in many cases, representative of existing societal biases. Researchers from Boston University and Microsoft have shown that software trained with text collected from Google News reproduced gender biases. When asked to complete the statement "Man is to computer programmer as woman is to [blank]," the trained software responded with "homemaker." Female representation is important in the fields of ML and AI to highlight, interrogate, and correct biases such as the ones implicit in the previous example.

In the 1980s, a bioengineer named Norm Heglund was doing field work in Kenya, hoping to uncover the secrets of locomotion. Heglund and his team spent their days shooting wild animals with tranquilizer darts in Kenya's national parks then dragging them back to a research station, run by the East Africa Veterinary Research Organization, in nearby Muguga for testing. Every day, the wives of local colleagues stopped by the lab to chit chat. They carried impossibly huge bundles of food, clothing, or other supplies perfectly balanced on top of their heads. During one lunch break, a few weeks into his stay, he realized something.