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 african context


AfriStereo: A Culturally Grounded Dataset for Evaluating Stereotypical Bias in Large Language Models

Beux, Yann Le, Audu, Oluchi, Ankeli, Oche D., Balakrishnan, Dhananjay, Weya, Melissah, Ralaiarinosy, Marie D., Ezeani, Ignatius

arXiv.org Artificial Intelligence

Existing AI bias evaluation benchmarks largely reflect Western perspectives, leaving African contexts underrepresented and enabling harmful stereotypes in applications across various domains. To address this gap, we introduce AfriStereo, the first open-source African stereotype dataset and evaluation framework grounded in local socio-cultural contexts. Through community engaged efforts across Senegal, Kenya, and Nigeria, we collected 1,163 stereotypes spanning gender, ethnicity, religion, age, and profession. Using few-shot prompting with human-in-the-loop validation, we augmented the dataset to over 5,000 stereotype-antistereotype pairs. Entries were validated through semantic clustering and manual annotation by culturally informed reviewers. Preliminary evaluation of language models reveals that nine of eleven models exhibit statistically significant bias, with Bias Preference Ratios (BPR) ranging from 0.63 to 0.78 (p <= 0.05), indicating systematic preferences for stereotypes over antistereotypes, particularly across age, profession, and gender dimensions. Domain-specific models appeared to show weaker bias in our setup, suggesting task-specific training may mitigate some associations. Looking ahead, AfriStereo opens pathways for future research on culturally grounded bias evaluation and mitigation, offering key methodologies for the AI community on building more equitable, context-aware, and globally inclusive NLP technologies.


AI and the Future of Work in Africa White Paper

O'Neill, Jacki, Marivate, Vukosi, Glover, Barbara, Karanu, Winnie, Tadesse, Girmaw Abebe, Gyekye, Akua, Makena, Anne, Rosslyn-Smith, Wesley, Grollnek, Matthew, Wayua, Charity, Baguma, Rehema, Maduke, Angel, Spencer, Sarah, Kandie, Daniel, Maari, Dennis Ndege, Mutangana, Natasha, Axmed, Maxamed, Kamau, Nyambura, Adamu, Muhammad, Swaniker, Frank, Gatuguti, Brian, Donner, Jonathan, Graham, Mark, Mumo, Janet, Mbindyo, Caroline, N'Guessan, Charlette, Githinji, Irene, Makhafola, Lesego, Kruger, Sean, Etyang, Olivia, Onando, Mulang, Sevilla, Joe, Sambuli, Nanjira, Mbaya, Martin, Breloff, Paul, Anapey, Gideon M., Mogaleemang, Tebogo L., Nghonyama, Tiyani, Wanyoike, Muthoni, Mbuli, Bhekani, Nderu, Lawrence, Nyabero, Wambui, Alam, Uzma, Olaleye, Kayode, Njenga, Caroline, Sellen, Abigail, Kairo, David, Chabikwa, Rutendo, Abdulhamid, Najeeb G., Kubasu, Ketry, Okolo, Chinasa T., Akpo, Eugenia, Budu, Joel, Karambal, Issa, Berkoh, Joseph, Wasswa, William, Njagwi, Muchai, Burnet, Rob, Ochanda, Loise, de Bod, Hanlie, Ankrah, Elizabeth, Kinyunyu, Selemani, Kariuki, Mutembei, Maduke, Angel, Kiyimba, Kizito, Eleshin, Farida, Madeje, Lillian Secelela, Muraga, Catherine, Nganga, Ida, Gichoya, Judy, Maina, Tabbz, Maina, Samuel, Mercy, Muchai, Ochieng, Millicent, Nyairo, Stephanie

arXiv.org Artificial Intelligence

This white paper is the output of a multidisciplinary workshop in Nairobi (Nov 2023). Led by a cross-organisational team including Microsoft Research, NEPAD, Lelapa AI, and University of Oxford. The workshop brought together diverse thought-leaders from various sectors and backgrounds to discuss the implications of Generative AI for the future of work in Africa. Discussions centred around four key themes: Macroeconomic Impacts; Jobs, Skills and Labour Markets; Workers' Perspectives and Africa-Centris AI Platforms. The white paper provides an overview of the current state and trends of generative AI and its applications in different domains, as well as the challenges and risks associated with its adoption and regulation. It represents a diverse set of perspectives to create a set of insights and recommendations which aim to encourage debate and collaborative action towards creating a dignified future of work for everyone across Africa.


Can an AI Win Ghana's National Science and Maths Quiz? An AI Grand Challenge for Education

Boateng, George, Kumbol, Victor, Kaufmann, Elsie Effah

arXiv.org Artificial Intelligence

There is a lack of enough qualified teachers across Africa which hampers efforts to provide adequate learning support such as educational question answering (EQA) to students. An AI system that can enable students to ask questions via text or voice and get instant answers will make high-quality education accessible. Despite advances in the field of AI, there exists no robust benchmark or challenge to enable building such an (EQA) AI within the African context. Ghana's National Science and Maths Quiz competition (NSMQ) is the perfect competition to evaluate the potential of such an AI due to its wide coverage of scientific fields, variety of question types, highly competitive nature, and live, real-world format. The NSMQ is a Jeopardy-style annual live quiz competition in which 3 teams of 2 students compete by answering questions across biology, chemistry, physics, and math in 5 rounds over 5 progressive stages until a winning team is crowned for that year. In this position paper, we propose the NSMQ AI Grand Challenge, an AI Grand Challenge for Education using Ghana's National Science and Maths Quiz competition (NSMQ) as a case study. Our proposed grand challenge is to "Build an AI to compete live in Ghana's National Science and Maths Quiz (NSMQ) competition and win - performing better than the best contestants in all rounds and stages of the competition." We describe the competition, and key technical challenges to address along with ideas from recent advances in machine learning that could be leveraged to solve this challenge. This position paper is a first step towards conquering such a challenge and importantly, making advances in AI for education in the African context towards democratizing high-quality education across Africa.


Artificial Intelligence

#artificialintelligence

We may think of Artificial Intelligence as some obscure, futuristic concept of the Fourth Industrial Revolution that is associated with scenes from "out-there" sci-fi movies like Star Wars and Terminator. But AI is already here and being used in a wide variety of technological developments that make our daily responsibilities more efficient and convenient. In fact, scientists believe we are already'cyborgs' because we already have a'digital self', which lives into perpetuity. Artificial intelligence (AI) is intelligence demonstrated by machines and contrasts with natural intelligence displayed by humans. Towards Data Science says "Artificial Intelligence is the ability of a computer program to learn and think. Everything can be considered Artificial intelligence if it involves a program doing something that we would normally think would rely on the intelligence of a human."


Google To Establish Artificial Intelligence Research Center In Ghana

#artificialintelligence

Google has announced that it will establish an artificial intelligence research center in the Ghanaian capital later this year -- a first for the company on the continent. Expert machine learning researchers and engineers will work together at the new AI research center in Accra which will be dedicated to artificial intelligence research and its applications in a wider African context, according to Quartz. The research center will focus on using artificial intelligence applications in areas that include healthcare, agriculture and education. Announcing the AI research center in a Google blog post, senior Google AI fellow Jeff Dean and staff research scientist Moustapha Cisse wrote about the growing interest artificial intelligence has experienced across Africa. "In recent years, we've witnessed an increasing interest in machine learning research across the continent," the pair wrote in the Google blog post.