{"id":19209,"date":"2023-11-02T17:36:19","date_gmt":"2023-11-02T16:36:19","guid":{"rendered":"https:\/\/grain-africa.org\/?p=19209"},"modified":"2023-11-02T17:36:19","modified_gmt":"2023-11-02T16:36:19","slug":"comprendre-les-biais-de-genre-dans-la-construction-de-modeles-dintelligence-artificielle-dans-le-contexte-africain","status":"publish","type":"post","link":"https:\/\/grain-africa.org\/en\/comprendre-les-biais-de-genre-dans-la-construction-de-modeles-dintelligence-artificielle-dans-le-contexte-africain\/","title":{"rendered":"Understanding gender bias in the development of artificial intelligence models in the African context"},"content":{"rendered":"

Country: Uganda<\/strong>\u00a0<\/h3>\n\n\n\n

Project summary <\/strong><\/h3>\n\n\n\n

Machine learning technologies such as automatic speech recognition systems, language models and machine translation systems are developed to operate in environments where gender and other forms of bias and discrimination are present.<\/p>\n\n\n\n

These biases are present in the datasets used to train models and in the choices made regarding machine learning models. The ML models that are developed ultimately reinforce the biases embedded in the training data.<\/p>\n\n\n\n

This research will focus on a data-driven approach to conduct a scoping study aimed at gaining a deeper understanding of gender bias and the inclusivity of artificial intelligence tools on the African continent. The findings will be used to develop a framework and guidelines for mitigating gender bias in ASR systems through fairness in data collection and model development.<\/p>\n\n\n\n

Finally, this work will develop a model governance framework to address the ethical and gender-related issues surrounding the data and algorithms on which ASR systems are based.<\/p>\n\n\n\n

Project implementation area:<\/strong> Uganda<\/h3>\n\n\n\n

Project lead:<\/strong> Dr Joyce Nakatumba-Nabende<\/h3>\n\n\n\n

Joyce Nakatumba-Nabende is a senior lecturer in the Department of Computer Science. She heads the Makerere Artificial Intelligence Laboratory, where she has worked on research projects aimed at developing and applying artificial intelligence and machine learning methods and tools to improve quality of life, particularly in developing countries. She is a member of the board of directors of Data Science Africa and a member of the ACM\u2019s Education Advisory Committee.<\/p>\n\n\n\n

Team members <\/strong><\/h3>\n\n\n\n

Andrew Katumba<\/strong><\/p>\n\n\n\n

Andrew Katumba is a senior lecturer in the Department of Electrical and Computer Engineering at Makerere University. His current research focuses on the application of machine learning and data science techniques in fields relevant to the African context. Key examples include: 1) the development of a machine learning-assisted smartphone platform for the diagnosis of cervical cancer, 2) the development of a portable, machine learning-assisted lung ultrasound system, 3) the development of a diagnostic platform for plant diseases; 4) the development of machine learning-assisted NLP tools for low-resource languages.<\/p>\n\n\n\n

Dr Peter Nabende<\/strong><\/p>\n\n\n\n

Peter Nabende is a senior lecturer in the Department of Information Systems at the School of Computing and Informatics Technology, Makerere University. His research focuses on natural language processing, applications of artificial intelligence for the developing world, and data mining. He has worked on research projects aimed at developing resources and models for applications in resource-constrained African contexts. He has also developed a short tutorial on eliminating or mitigating bias in AI training data.<\/p>\n\n\n\n

Eric Peter Wairagala<\/strong><\/p>\n\n\n\n

Eric Peter Wairagala is a research software engineer at the AI and Data Science Research Laboratory at Makerere University. Highly motivated and passionate, he is currently studying for a Master\u2019s degree in Computer Science at Makerere University, specialising in AI and data science. His research focuses on the fields of computer vision, machine learning and natural language processing, with a particular emphasis on responsible AI. He collaborates with the Masakhane, Data Science Africa and Deep Learning Indaba communities, which aim to bridge the gap between AI and machine learning in Africa.<\/p>\n\n\n\n

Tobius Saul Bateesa<\/strong><\/p>\n\n\n\n

Tobius Saul Bateesa is a research assistant and software engineer at the Makerere Artificial Intelligence and Data Science Laboratory. At the Makerere AIR Lab, he has worked on projects that use data science and artificial intelligence to tackle challenges facing developing countries in fields such as agriculture, health, urban planning, ethics and cultural preservation. He is currently studying for a Master\u2019s degree in Data Science at the University of East London, where he is continuing to develop his expertise.<\/p>\n\n\n\n

Carol Kantono<\/strong><\/p>\n\n\n\n

Caroline Kantono is a research assistant at the Artificial Intelligence and Data Science Laboratory at Makerere University. She is pursuing a career in data science after completing a Master\u2019s degree in Business Intelligence and Analytics at the University of Applied Sciences in Neu-Ulm. Her research focuses on natural language processing (NLP) and artificial intelligence, and she has completed her research project on sentiment analysis.<\/p>\n\n\n\n

Description of the institution<\/strong><\/p>\n\n\n\n

The Makerere Artificial Intelligence Laboratory is a research laboratory at Makerere University specialising in the application of artificial intelligence and data science to problems facing the developing world. The Artificial Intelligence Laboratory has over ten years\u201c experience in artificial intelligence research, with a focus on applications to local issues. Driven by our mission to \u201dAdvance artificial intelligence research to solve real-world challenges\u201d, and thanks to strong partnerships with national organisations and university departments such as the Institute of Languages, we have expertise in (a) developing tools for the collection and curation of datasets for downstream NLP tasks, (b) building computational models for downstream natural language processing tasks, (c) building computational models for image crowdsourcing and real-time monitoring, and (b) applying computer vision to the diagnosis of crop pests and diseases.<\/p>\n\n\n\n

Link to the institution: <\/strong>https:\/\/air.ug\/<\/strong><\/a><\/p>\n

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