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Announcing the #AI4D Africa Innovation 2019 Winners

The AI for Development (AI4D) Initiative is pleased to announce the winners of the AI4D-Africa Innovation Call for Proposals 2019.

Sign up and join us to celebrate the winners at  Deep Learning Indaba 2019 at the #AI4D Network of Excellence Innovation Grant Award Ceremony:

    • Tuesday, 27th August 2019 at 7 PM (Nairobi Time)
    • (LOCATION UPDATE) Interaction Hall – KUCC, Kenyatta University, Nairobi, Kenya.

The first named individual is the Principle Investigator. Funding for these innovation seed grants is made available with the support of Canada’s International  Development Research Centre. To learn more about our Network of Excellence in Artificial Intelligence for Development in Sub-Saharan Africa click here. 

Congratulations to all recipients. Follow us at @AI4Dev. 


Dr. Abdelhak Mahmoudi  
Mohammed V University of Rabat, Morocco
Arabic Speech-to-MSL Translator: ‘Learning for Deaf’
To develop an Arabic text to Moroccan Sign Language (MSL) translation product through building two corpora of data on Arabic texts for the use of translation into MSL. The collected corpora of data will train Deep Learning Models to analyze and map Arabic words and sentences against MSL encodings.


Dr. Adewale Akinfaderin, Olamilekan Wahab and Olubayo Adekanmbi
Data Duality Lab, Data Science Nigeria, MTN Nigeria, Nigeria
Using Artificial Intelligence to Digitize Parliamentary Bills in Sub-Saharan Africa 
To improve and expand the categorizing of parliamentary bills in Nigeria using Optical Character Recognition (OCR), document embedding, and recurrent neural networks to three other countries in Africa: Kenya, Ghana, and South Africa. 


Dr. Amelia Taylor, Eva Mfutso-Bengo and Binart Kachule
University of Malawi and the Polytechnic, University of Malawi, Malawi
A Semi-Automatic Tool for Meta-Data Extraction from Malawi Court Judgments 
To develop a methodology for a semi-automatic classification of judgments disseminated by the High Court Library of the Malawi Judiciary with the purpose of enabling ‘intelligent searching’ within this body of knowledge.


Dr. Aminata Zerbo Sabane, Dr. Tegawendé Bissyande, and T. Idriss Tinto 
L’université Joseph Ki-Zerbo and La Communauté Afrique Francophone des Données Ouvertes, Burkina Faso
Preservation of Indigenous Languages 
To initiate a research roadmap for the preservation of indigenous languages through the means of collecting, categorizing and archiving of translation and voice synthesis to perform the automatic translation in official and indigenous languages. 


Denis Pastory Rubanga, Dr. Zekaya Never, Dr. Machuve Dina, Lilian Mkonyi, Loyani K. Loyani, Richard Mgaya.
Tokyo University of Agriculture, The Nelson Mandela African Institution of Science and Technology, and Sokoine University of Agriculture, Tanzania
A Computer Vision Tomato Pest Assessment and Prediction Tool    
Pest monitoring by using a data-driven computer vision technique in directing the extension officers support services across sub-Sahara Africa in a real-time pest damage assessment and recommendation support system for small scale tomato farmers.


Martha Shaka, Nyamos Waigama, Emilian Ngatunga, Halidi Maneno, Said Said, Said Mmaka, Frederick Apina, Simon Chaula, Emani Sulutya, Merikiadi Mashaka
University of Dodoma and Benjamin Mkapa Hospital, Tanzania
Effective Creation of Ground Truth Data-Set for Malaria Diagnosis Using Deep Learning 
To create an automatic data annotation tool and ground truth dataset for malaria diagnosis using deep learning. The ground truth dataset and the tool will streamline the development of AI tools for pathology diagnosis.


Dr. Moes Thiga and Dr. Pamela Kimeto
Kabarak University, Kenya
Early Detection of Pre-Eclampsia Using Wearable Devices and Long Short Term Memory Networks
To determine the effectiveness of Long Short Term Memory Network in the prediction of pregnant mothers at high risk of developing pre-eclampsia and the effectiveness of prophylaxis of preeclampsia.


Ronald Ojino and Khushal Brahmbhatt
Cooperative University of Kenya, Kenya
A Public Dataset on Poaching Trends in Kenya and a Study on the Predictive Modeling of Poaching Attacks
To test the feasibility of the deployment of Unmanned Ground Vehicles (UGVs) for automated intelligent patrol, detection, wildlife monitoring, identification across the national parks and reserves in Kenya. 


Steven Edward, Edward James, and Deo Shao
Nelson Mandela African Institute of Science and Technology, Tanzania
Improving the Pharmacovigilance system using Natural Language Processing  on Electronic Medical Records
To improve the pharmacovigilance system by proposing a novel algorithm for the auto-extract of adverse drug reaction cases from Electronic Medical Records and reduce the time taken and introduce the confidentiality of reporting.


Dr. Tegawendé F. Bissyande, Dr. Aminata Zerbo Sabane, and T. Idriss Tinto 
Université Joseph Ki-Zerbo and La Communauté Afrique Francophone des Données Ouvertes, Burkina Faso
Building a Medicinal Plant Database for Preserving Ethnopharmacological Knowledge in the Sahel 
To initiate the collection and construction of a medicinal plant database on top of which a search engine and AI-based image recognition for plants to enable scalable search of preserved knowledge.

A roadmap for artificial intelligence for development in Africa

Based on the workshop ” Toward a Network of Excellence in Artificial Intelligence for Development (AI4D) in sub-Saharan Africa” – April 3-5, 2019 Nairobi, Kenya.


Artificial intelligence is poised to enhance productivity, innovation and help countries across sub-Saharan Africa to achieve the Sustainable Development Goals – helping to improve outcomes from health care to agriculture to education. Yet as with any technology, the transformative potential benefits come with challenges that need to be managed and moderated. Though countries across sub-Saharan Africa are on this threshold of harnessing AI technologies to support genuine human development and bolster economic and political progress, there are likely to be ramifications on already precarious livelihoods, labour markets, and fragile governing institutions. Addressing these opportunities and challenges are key to the roadmap for AI for development.  

The AI Readiness Index 2018 (forthcoming)
The AI Readiness Index 2018 (forthcoming)

Infrastructural challenges, data gaps, highly skilled labour needs, and poor regulatory environments are still inhibiting people’s ability to harness AI across Africa. A global review of secondary data available on AI suggests that the continent needs to support better infrastructure and fill data gaps, and support education and capacity building to ensure there are enough highly skilled researchers able to implement AI solutions. Moreover, a critical dimension is the legal, ethical and human rights-based frameworks needed for countries to optimize the best uses of AI – frameworks that will protect citizens’ data and build trust and legitimacy for AI and the multitude of applications. These are still largely absent.

In the next five years, what would happen if we had more than 30 African countries developing their own AI policies and strategies? Imagine if there are more than 400 PhDs in AI and machine learning from across the continent? And what if universities, the private sector, and other public interest institutions invest a billion dollars in collaborations on AI to support the African sustainable development agenda?

These proposals are now emerging from the African AI community after the recent workshop in Nairobi on Artificial Intelligence for Development in Sub-Saharan Africa. The meeting gathered sixty African and international experts together to demonstrate and discuss how people across the continent are developing AI at a rapid pace. There is a vibrant AI ecosystem emerging across the continent with initiatives like Deep Learning Indaba, the African Institute for Mathematical Science (AIMS), Data Science Africa, and Women+ in Machine Learning and Data Science (WIMLDS) — all of which aim to strengthen machine learning and artificial intelligence in Africa.

To support the growing momentum in Africa’s emerging AI ecosystem, this emerging ‘network of excellence’ of machine learning and AI practitioners and researchers is building a collaborative roadmap for AI for Development in Africa. The three-day workshop focused in on three critical areas of 1) policy and regulations, 2) skills and capacity building and 3) the application of AI in Africa. The following slides and the analysis below synthesize the participatory discussions during the workshop on these three critical areas.  

Artificial Intelligence Network of Excellence in Sub-Saharan Africa – Nairobi Workshop 2019

Policy and regulations

“Thirty African countries should develop AI specific policies and strategies over the next five years”

Currently, out of the 46 sub-Saharan African states, only Kenya has an AI taskforce that is working towards a national strategy. There is a clear need to build policy capacity as well to help design AI regulatory frameworks fit for the African context. An area of convergence in this discussion was the need to channel investments toward prioritized solutions for development, but also the need to reflect and better understand what “development” means. The pan-African development blueprint – the African Union’s Agenda 2063 – could offer such a shared vision. Other ideas emerged during the workshop, including collaboration within the network to inform and grow AI-specific policies to at least 30 African countries over the next five years, and increasing the adoption of the AU’s Convention on Cyber Security and Personal Data Protection. There is also critical recognition that the copy-and-paste of made-in-the-North AI policy frameworks will not serve the needs of Africans. No one country has all the solutions. So while policy options and best practices from other parts of the worlds can provide great sources of inspiration and guidance, the priorities and directions must be determined locally in a way that facilitates innovation, curbs any harms and upholds human rights.

Skills and infrastructure

“Create a pipeline of 400 African PhDs in AI, data science, and other interdisciplinary fields.”

Building an inclusive AI future in Africa means we must build AI strength through community. Skills and capacity received a lot of attention during the three-day workshop. One of the participants at the workshop shared that there is a need to “better formalize AI training and the recruitment of AI talent in Africa” and to create a pipeline of 400 African PhDs in AI, data science, and other interdisciplinary fields over the next five years. This means having the appropriate infrastructure to build this capacity.

Furthermore, there is the need to enhance the strength and agility of educational systems to meet new digital opportunities, and support investments in learning outcomes in the areas of machine learning, artificial intelligence, and data sciences in Africa. As one participant said, “Let’s think beyond PhDs. We should also target and invest in youth at the primary school level, offer mentorship programs for emerging leaders, including for women and girls to build the next generation of AI practitioners, and create opportunities to bring more of the public into the AI conversation.” Moving forward, inclusion must remain a core principle in guiding the design of responsive and equitable AI skills and capacity building roadmap in Africa.

The hope is that it will be possible to establish an AI Centre of Excellence in every African country by 2030 that will help incubate ideas and foster AI communities of practice in an interdisciplinary and inclusive matter.


“A collective investment of USD 1 billion dollars in collaborative innovation and research prioritizing solution areas for sustainable development in Africa.”

The application of AI must be connected to people and their needs on the ground, and the potential benefits of AI translated into real impact for people. The ground-up approach of this collaborative workshop was an excellent way to start drawing fresh ideas and practical use cases on how AI could help increase access to healthcare and education for the most vulnerable in rural areas, and improve the movement of people, goods, and ideas especially within and between rapidly growing megacities across Africa.

A key idea discussed among participants was how to mobilize collaboration among a network of African companies, universities, research centres, and public institutions to collaborate on advancing the AI research for development agenda. Participants suggested a collective investment of USD 1 billion dollars in collaborative innovation and research prioritizing solution areas for sustainable development in Africa. These partnerships could mean when Africans design and deploy AI applications, societal goals and human rights commitments like decent work conditions and gender equality are integrated into projects from the beginning.

Next steps

It is very clear from the diverse range of voices at the workshop that we cannot forget that within Africa’s emerging and vibrant AI ecosystem, the inclusion and diversity of voices from traditionally marginalized communities must be prioritized. And there is a need to expand the AI Network of Excellence conversation to Francophone and Portuguese-speaking countries on the continent as well. It is also critical to understand that contexts differ in Kenya, Nigeria, and South Africa and even more so in places where AI is still unknown, so strategies and engagement must be tailored to local situations.

The enriching and collaborative dialogue over the three days of the AI4D workshop has built a solid foundation for the research and innovation agenda in Africa. Progress on this agenda will depend on the collective action of many actors to hone and support this vision, and we will continue to engage with partners and stakeholders in shaping an inclusive AI ecosystem in Africa.  

See also:


We would like to thank all the participants who contributed their voices and ideas during this event. This three-day dialogue was organized by the International Development Research Centre, Swedish International Development Agency, Knowledge for All Foundation and the Strathmore University in Nairobi, Kenya. Follow us on Twitter at @AI4Dev


lnteligencia Artificial y Desarrollo en America Latina: bases para una iniciativa regional

El crecimiento de la potencia computacional, la disponibilidad de grandes cantidades de datos abiertos y el progreso de los algoritmos han hecho que la Inteligencia Artificial (IA) se convierta en una de las tecnologías más prometedoras y desafiantes del siglo XXI. Sin embargo, varios países de la región de América Latina y el Caribe (ALC) apenas están generando conciencia sobre la importancia de estos desarrollos, y pocos han explorando el desarrollo de políticas nacionales aplicables a la inteligencia artificial. En este proceso, existe una desconexión importante entre la comunidad de práctica de la IA y las comunidades en desarrollo.

Dado este contexto, el Centro Latam Digital y el Centro Internacional de Investigaciones para el Desarrollo (IDRC) organizaron conjuntamente un taller con invitación para establecer colectivamente con actores clave un proyecto regional de Inteligencia Artificial para el Desarrollo.

El taller se llevó a cabo los días 25 y 26 de febrero de 2019 en el Centro de Investigación y Docencia Económicas (CIDE) en la Ciudad de México. El taller se desenvolvió como un intercambio abierto con la colaboración de la Iniciativa Latinoamericana para Datos Abiertos (ILDA) a través de sesiones plenarias y discusiones en grupos de trabajo con más de 30 expertos de nueve países de la región. Los participantes fueron seleccionados en función de sus antecedentes y conocimientos asociados al tema en cuestión.

Los objetivos del taller fueron:

  • Reunir a una serie de actores clave de las diversas comunidades involucradas en el desarrollo o producción, investigación, regulación y uso de la IA para compartir conocimiento e identificar áreas prioritarias claves de investigación y capacitación para la región.
  • Proporcionar directrices sobre la estructura y los objetivos potenciales de una iniciativa para apoyar el desarrollo de políticas nacionales sólidas sobre la IA en la región.
  • Establecer la base de una comunidad regional de responsables políticos, investigadores, empresas e instituciones del sector privado y organizaciones no gubernamentales orientadas al desarrollo y la implementación de la

Este blog resume las ideas más importantes del taller junto con los pasos a seguir hacia la construcción de una red de expertos y organizaciones que responden a la evolución reciente de las tecnologías de la información y comunicación (TIC) en América Latina e impulsen la IA como un vehículo para el desarrollo social y económico de la región.


 El enfoque durante el primer día fueron los desafíos de la inteligencia artificial en la región latinoamericana. Los participantes en el grupo coincidieron en que los gobiernos no reconocen a la IA como una solución potencial para los asuntos urgentes del desarrollo social y económico de su país. Es necesario crear un mayor entendimiento del impacto potencial de la adopción de la IA como un medio para la solución de problemas prioritarios en las agendas nacionales de desarrollo. El contexto latinoamericano enfrenta una falta de conciencia en el sector público sobre la IA; se trabaja de forma reactiva, vertical, en silos y sin convergencia.

Aunque exista un creciente acervo de informes y recursos para el análisis de la importancia de las agendas digitales nacionales, pocas veces se traducen en acciones por parte de los gobiernos debido a la falta de capacidad, estructura legal y autoridad para llevar a cabo la implementación en un contexto local específico con recursos limitados.

Los participantes estaban de acuerdo con que el gobierno es un actor esencial para impulsar políticas públicas eficientes para el aprovechamiento de la IA como habilitador del desarrollo de largo plazo. Sin embargo, los gobiernos necesitan marcos éticos de gobernanza de datos, lineamientos para la protección de datos y privacidad de información, una mejor distribución de recursos y de mecanismos para dar continuidad a una agenda digital y el uso de IA como habilitador.

Los siguientes puntos fueron planteados por los participantes.

  • Es clave definir una agenda en común entre actores en la región para plantear la IA como una capacidad transversal, no un producto, para impulsar el desarrollo sostenible.
  • Para definir una agenda accionable, es importante impulsar plataformas multiactor para dialogar y conocer a los actores a nivel regional y entender sus capacidades en distintos sectores, así como realizar un diagnóstico para para entender qué herramientas ya
  • Es necesario no solo fortalecer las capacidades de investigación en temas de IA, sino también hacer un vínculo entre académicos y empresas hasta complementar e equilibrar capacidades. Si complementamos capacidades, más fuerza tenemos para crear alianzas para desarrollar iniciativas alrededor de problemas críticos de los países de la región.
  • Es clave generar una demanda ciudadana para una agenda nacional de IA a través de
  • Es sumamente importante diagnosticar la etapa de madurez de IA en la región, para entender la oportunidad de fortalecer capacidades desde alfabetización digital hasta

gestión de proyectos de IA, articulación en políticas públicas, la capacidad técnica y acceso y manejo de infraestructura en los varios sectores.

Caminos por seguir

Los participantes buscaran definir posibles estrategias alrededor de tres puntos de intervención: diseño e influir en la política pública, desarrollo de habilidades, y aplicaciones de IA en la región. Conforme a la idea presentada por los representantes de IDRC, las intervenciones serían diseñados y ejecutados por una red regional de especialistas y organizaciones liderando temas relacionados con la IA.

Política pública

Para influir en la política pública, se acordó que es importante realizar un mapeo de iniciativas y políticas en la región para tener un panorama claro de los vacíos de conocimiento, capacidades, niveles de adopción y recursos asignados alrededor de IA. También se tendría que identificar dos a tres puntos de incidencia, posiblemente en el marco de gobierno digital o problemas sectoriales concretos que necesiten soluciones de corto plazo, para demostrar el potencial de impacto de la adopción de IA en la eficacia y eficiencia de programas gubernamentales. Para vigilar e informar sobre estrategias y políticas de IA, se podría crear un observatorio que también desarrolle una metrica de madurez de adopción nacional y subnacional en la región. Por último, es importante ofrecer actividades de capacitación presenciales y en línea de IA para gobiernos y definir esquemas de incentivos para su participación.

Desarrollo de habilidades

En el ámbito de desarrollo de habilidades, se propone crear un centro de investigación de alta calidad que estructure programas para crecer y retener talento para contribuir a la IA aplicada para abordar los retos al desarrollo en la región. Este centro se conecta a universidades, gobierno, industria. Cuenta con una agenda de investigación interdisciplinaria y un área de emprendimiento para contribuir al diseño de las soluciones para el desarrollo . Se comentó que al tener un centro de IA aplicada basada en la región, esta iniciativa puede tener un efecto secundario a la educación alrededor de alfabetización digital, STEM y generar entusiasmo en el sistema educativa.


Se deben definir áreas (como educación, agricultura, violencia, salud) para la aplicación de IA usando un marco de alto nivel como Objetivos de Desarrollo Sostenible que permiten adaptarse en contextos locales. Género debe ser un tema transversal que se integre en todas las áreas en las que se aplicaría intervenciones de IA. Para definir estas áreas, es clave realizar un mapeo de actores que participen en la selección de temas y ayuden identificar las iniciativas que deben ser elegidas a través de una serie de criterios. También se debe identificar mejores prácticas en IA para facilitar la réplica de iniciativas a través de estandarizar y proporcionar paquetes de herramientas para la implementación. Se propuso la idea de tener 3-4 estudios de caso en los primeros dos años del proyecto, y en cinco años tener resultados más tangibles con métricas e indicadores de impacto tales como niveles de bienestar, capacidades, conciencia y percepción del potencial de la inteligencia artificial entre otros.


Los participantes de este taller demostraron un gran interés en identificar oportunidades para la colaboración, la creación de redes y el compromiso de políticas en torno a la inteligencia artificial para el desarrollo en la región. Es necesario continuar con las discusiones y fortalecer vínculos entre las instituciones participantes para dar continuidad a las agendas discutidas. Se alentó a los participantes a identificar los próximos pasos en los que se pueda actuar.

Conforme a las conclusiones del taller, se desarrollará una red formal para sostener esta comunidad de especialistas. Esta red contará con una marca, estrategia y objetivos definidos, y un proceso de integración de nuevos actores para expandir a nivel país y regional a una escala manejable. A través de esta red, se definirán estrategias para compartir hitos, eventos, iniciativas de ley, contactos y oportunidades de financiamiento a través del uso de herramientas como Google Drive, Slack, una página web, y las redes sociales. Se desarrollará una ruta crítica para la red junto con un plan de comunicación para asegurar el seguimiento de las conversaciones acerca este proyecto. Un vez confirmado el cronograma y el presupuesto de IDRC, se organizarán reuniones de trabajo subsecuentes.

Centro Latam Digital e ILDA facilitarán conversaciones en curso entre los participantes a través de un canal de AI4D en SLACK para dar continuidad a la formación de una red de expertos, legisladores, investigadores y tecnólogos que han contribuido a este diálogo sobre inteligencia artificial para el desarrollo de América Latina.

Organizado por:

December Review; AI4D- African Language Dataset Challenge // Bilan de decembre; Défi AI4D – Jeu de Données sur les Langues Africaines

The close of 2019 marked the second month of the AI4D African Dataset Challenge, an effort aimed at incentivizing the uncovering and creation of African language datasets for improved representation in NLP. This challenge is hosted on Zindi and has been ongoing since the 1st of November. Each month we take stock and award a total of USD 1000 to the two most outstanding submissions.

In December, these two were as follows;

  • A Yoruba dataset submitted by David Adelani. This submission was put together by three individuals, David, Damilola Adebonojo and Omo Yooba, the latter two of whom are major Yoruba contributors for Global Voices Lingua, a movement which aims to bridge worlds and amplify voices through translating stories into dozens of languages. Beyond including some of the news stories from the Global Voices website, they translated several chapters of a book, got parallel sentences from a Twitter account that posts Yoruba proverbs, translated part of a movie dialogue found on YouTube and supplemented these with multi-domain sentences containing scientific and medical terms to work towards a representative dataset.
  • A Fongbe submission composed of datasets prepared for two tasks; 
    • Fongbe-French Machine Translation with data sourced from Bible translations, scraping a website and translating a book freely available online.
    • Automatic Speech Transcription data consisting of phoneme labels, single-speaker audio sentences as well as multi-speaker conversational audios.

We received 6 submissions in December, composed of data from 4 languages, Fongbe, Igbo, Swahili and Yoruba. This brings our overall language total, taking into consideration November and December submissions, to 6; Fongbe, Hausa, Igbo, Swahili, Wolof and Yoruba.

We observed one novel data collection process that involved first scanning text from a book containing a collection of folk-tales then digitizing these using Google’s Text Recognition software for Optical Character Recognition(OCR).  There was also a notable submission of Igbo names, a valuable resource that can be incorporated into the task of Named Entity Recognition. To learn more about other techniques being to create datasets, be sure to check the November round-up here.

As we begin evaluation of the January submissions, we continue to be impressed by the calibre of datasets submitted and the effort put into their creation. 

This work actively challenges us to think deeper about the various copyright implications of some of these data collection sources and processes and the modality of finally making all this data open. In addition to the choice of dataset to use for a Machine Learning task in the second phase of this challenge, as each month brings us closer to the end of the dataset creation phase.

Contribution by:
Kathleen Siminyu, AI4D-Africa Network Coordinator
Sackey Freshia, Jomo Kenyatta University of Agriculture and Technology
Daouda Tandiang Djiba, GalsenAI

La fin de l’année 2019 a marqué le deuxième mois du défi AI4D African Dataset Challenge, un effort visant à encourager la découverte et la création de jeux de données sur les langues africaines pour une meilleure représentation en NLP. Ce défi est hébergé sur Zindi et se déroule depuis le 1er novembre. Chaque mois, nous faisons le point et attribuons un total de 1000 USD aux deux meilleures soumissions.

En décembre, il s’agissait des deux suivantes ;

  • Un jeu de données Yoruba soumis par David Adelani. Cette soumission a été réalisée par trois personnes, David, Damilola Adebonojo et Omo Yooba, ces deux derniers étant des contributeurs yorubas majeurs pour Global Voices Lingua, un mouvement qui vise à rapprocher les mondes et à amplifier les voix en traduisant des histoires dans des dizaines de langues. En plus d’inclure certains des articles du site web de Global Voices, ils ont traduit plusieurs chapitres d’un livre, obtenu des phrases parallèles d’un compte Twitter qui publie des proverbes yorubas, traduit une partie d’un dialogue de film trouvé sur YouTube et complété ces derniers par des phrases multi-domaines contenant des termes scientifiques et médicaux pour travailler sur un jeu de données représentatif.
  • Une soumission Fongbe composée d’un jeu de données préparées pour deux tâches ; 
    • La traduction automatique Fongbe-français avec des données provenant de traductions de la Bible, en grattant un site web et en traduisant un livre disponible gratuitement en ligne.
    • Données de transcription automatique de la parole comprenant des étiquettes de phonèmes, des phrases audio à un seul locuteur ainsi que des audios conversationnels à plusieurs locuteurs.


Nous avons reçu 6 soumissions en décembre, composées de données provenant de 4 langues, le fongbe, l’igbo, le swahili et le yoruba. Cela porte à 6 le nombre total de langues, en tenant compte des contributions de novembre et de décembre : le fongbe, le haoussa, l’igbo, le swahili, le wolof et le yoruba.

Nous avons observé un nouveau processus de collecte de données qui consistait à scanner le texte d’un livre contenant un ensemble de contes populaires, puis à numériser ces derniers à l’aide du logiciel de reconnaissance de texte de Google pour la reconnaissance optique de caractères (OCR). 

Il y a également eu une soumission notable de noms Igbo, une ressource précieuse qui peut être incorporée dans la tâche de reconnaissance des entités nommées. Pour en savoir plus sur les autres techniques de création de jeu de données, consultez le résumé de novembre ici.

Alors que nous commençons l’évaluation des soumissions de janvier, nous continuons à être impressionnés par la qualité des jeux de données soumis et par les efforts déployés pour leur création. 

Ce travail nous met activement au défi de réfléchir plus en profondeur aux diverses implications en matière de droits d’auteur de certaines de ces sources et processus de collecte de données et à la modalité de rendre enfin toutes ces données ouvertes. Outre le choix de l’ensemble de données à utiliser pour une tâche d’apprentissage automatique dans la deuxième phase de ce défi, puisque chaque mois nous rapproche de la fin de la phase de création de l’ensemble de données.

Contribution de:
Kathleen Siminyu, Coordinatrice du réseau AI4D-Africa
Sackey Freshia, Jomo Kenyatta University of Agriculture and Technology
Daouda Tandiang Djiba, GalsenAI

November Review; AI4D- African Language Dataset Challenge // Bilan de novembre ; Défi AI4D – Jeu de Données sur les Langues Africaines



On the 1st of November, we launched the AI4D-African Language Dataset Challenge on Zindi, an effort towards incentivizing the uncovering and creation of African language datasets for improved representation in NLP. This first phase of what is expected to be a two-phase challenge, is taking place over 5 months, November 2019 to March 2020, with evaluation of submissions done on a monthly basis. Each month, the top 2 submissions will receive a cash prize of USD 500.

Being well into December we are excited to announce that the top two submissions for November were received from;

  • Oshingbesan Adebayo who submitted a dataset composed of three West African indigenous languages(Hausa, Igbo and Yoruba). The dataset was acquired from a wide variety of sources ranging from transcriptions of songs, online news sites, excerpts from published books, websites in indigenous languages to blogs, Twitter, Facebook and more. 
  • Thierno Diop who submitted an Automatic Speech Recognition dataset for Wolof in the domain of transportation services. The data was prepared through a collaboration between BAAMTU Datamation, a senegalease company focused on using data to help companies to leverage AI and Big Data, and WeeGo, an app which help passengers to get information about urban transport in Senegal.

Overall, we received 9 submissions in the month of November, composed of data from a total of 4 unique languages. These are Hausa, Igbo, Wolof and Yoruba.

Majority of the data came from online sources. Scraping of newspaper sites such as BBC, DW and VOA which curate news in several African languages emerged as one of the top ways that participants went about creating datasets. A great strategy for putting together a sizeable dataset over the coming months would be to keep going back to the site(s) every so often and keeping your dataset up to date with the site as news is regularly published. Capturing a wide variety of news categories would go a long way in ensuring the dataset is well balanced and representative of language variety. Wikipedia sites published in various languages also featured as a data source. 

  • BBC publishes news in Afaan Oromoo, Amharic, Hausa, Igbo, Kirundi, Pidgin, Somali, Swahili, Tigrinya and Yoruba 
  • DW publishes news in Amharic, Hausa and Kiswahili 
  • VOA publishes news in Afaan Oromoo, Amharic, Bambara, Hausa, Kinyarwanda/Kirundi, Ndebele, Shona, Somali, Kiswahili and Tigrinya

A closely related online source is Twitter data, which we have seen particularly curated for the task of sentiment analysis. A good place to start would be the accompanying Twitter profiles of the above news sites. While we haven’t had any data sourced from Facebook yet, I imagine that the profiles maintained by these news outlets for various languages would also be a good place to start.  

Manual translation also emerged with some submissions compiled as a result of one or several individuals coming together to translate pieces of text as well as custom applications such as mobile applications being used to crowdsource voice overs for the dataset created for Automatic Speech Recognition. 

I am also excited to announce that we will have a workshop at ICLR 2020, “AfricaNLP – Unlocking Local Languages”, which will be held in Addis Ababa in April of next year.
Part of the agenda of this workshop is set aside to showcase exceptional work and resulting datasets that will emerge as output from this exercise.

We will also use the workshop as an opportunity to launch the second phase of this challenge. If you have been following our thought process since the beginning, then you will know that the second phase of the challenge is largely dependent on the outcomes of this first phase. The one(or hopefully two) downstream NLP tasks that will be the object of the 2nd phase will utilise datasets that result from this first phase.

Finally, we have a Call for Papers for the workshop, specifically for research work involving African languages. Feel free to start making your submissions on this page. Here’s some key dates to keep in mind:

  • Submission deadline: 1st February, 2020
  • Notification to authors: 26th February, 2020
  • Workshop: 26th April, 2020

Happy Holidays!

Contribution by:
Kathleen Siminyu, AI4D-Africa Network Coordinator
Sackey Freshia, Jomo Kenyatta University of Agriculture and Technology
Daouda Tandiang Djiba, GalsenAI

Le 1er novembre, nous avons lancé le Défi AI4D – Ensemble de données sur les langues africaines sur Zindi, un effort pour encourager la découverte et la création  jeux de données sur les langues africaines pour une meilleure représentation en NLP. Cette première phase de ce qui devrait être un défi en deux phases, se déroule sur 5 mois, de novembre 2019 à mars 2020, avec une évaluation de la soumission faite sur une base mensuelle. Chaque mois, les deux meilleures soumissions recevront un prix en espèces de 500 USD.

Nous sommes heureux d’annoncer que les deux meilleures soumissions pour novembre ont été reçues ;

  • Oshingbesan Adebayo qui a soumis un jeu  de données composé de trois langues autochtones d’Afrique de l’Ouest (haoussa, igbo et yoruba). Le jeu  de données a été acquis auprès d’une grande variété de sources allant de transcriptions de chansons, de sites d’information en ligne, d’extraits de livres publiés, de sites Web en langues autochtones à des blogues, Twitter, Facebook et autres. 
  • Thierno Diop qui a soumis un ensemble de données de reconnaissance automatique de la parole pour le wolof dans le domaine des services de transport. Les données ont été préparées grâce à une collaboration entre BAAMTU Datamation, une société sénégalaise spécialisée dans l’utilisation des données pour aider les entreprises à tirer parti de l’intelligence artificielle et de Big Data, et WeeGo, une application qui aide les passagers à obtenir des informations sur le transport urbain au Sénégal.

Au total, nous avons reçu 9 soumissions au mois de novembre, composées de données provenant de 4 langues uniques au total. Il s’agit du haoussa, de l’igbo, du wolof et du yoruba.

La majorité des données provenaient de sources en ligne. Le grattage(scraping) de sites de journaux tels que la BBC, DW et VOA qui organisent des actualités dans plusieurs langues africaines est apparu comme l’un des principaux moyens utilisés par les participants pour créer des jeux  de données. Une excellente stratégie pour constituer un jeu de données important au cours des mois à venir serait de retourner sur le(s) site(s) de temps en temps et de garder le jeu de données à jour avec le site car des nouvelles sont régulièrement publiées. La saisie d’une grande variété de catégories de nouvelles contribuerait grandement à assurer que le jeu  de données est bien équilibré et représentatif de la variété des langues. Les sites Wikipédia publiés dans différentes langues sont également présentés comme une source de données. 

  • La BBC publie des nouvelles en afaan oromo, amharique, haoussa, igbo, kirundi, pidgin, somali, swahili, tigrinya et yoruba 
  • DW publie des nouvelles en Amharique, Hausa et Kiswahili 
  • VOA publie des informations en Afaan Oromoo, Amharique, Bambara, Haoussa, Kinyarwanda/Kirundi, Ndebele, Shona, Somali, Kiswahili et Tigrinya

Une source en ligne étroitement liée est celle des données de Twitter, que nous avons vu particulièrement bien conservée pour la tâche d’analyse des sentiments. Un bon point de départ serait les profils Twitter des sites d’information ci-dessus. Bien que nous n’ayons pas encore eu de données provenant de Facebook, j’imagine que les profils tenus par ces sites d’information dans différentes langues seraient également un bon point de départ.  

La traduction manuelle a également fait son apparition, certaines soumissions ayant été compilées à la suite de la collaboration d’une ou de plusieurs personnes pour traduire des morceaux de texte ainsi que des applications personnalisées telles que des applications mobiles utilisées pour créer des voix hors champ pour un ensemble de données créé pour la reconnaissance automatique de la parole. 

Je suis également heureux d’annoncer que nous aurons un atelier à la conférence ICLR 2020, “AfricaNLP – Unlocking Local Languages“, qui se tiendra à Addis-Abeba en avril prochain. Une partie de l’ordre du jour de cet atelier est réservée à la présentation des travaux exceptionnels et des jeux  de données qui résulteront et qui seront le fruit de cet exercice.

Nous profiterons également de l’atelier pour lancer la deuxième phase de ce défi. Si vous avez suivi notre processus de réflexion depuis le début, vous savez que la deuxième phase du défi dépend en grande partie des résultats de cette première phase. Les une (ou, espérons-le, deux) tâches de NLP en aval qui feront l’objet de la deuxième phase utiliseront les ensembles de données qui résultent de cette première phase.

Enfin, nous avons un appel à communications pour l’atelier, spécifiquement pour les travaux de recherche impliquant les langues africaines. N’hésitez pas à commencer à faire vos soumissions ici.

  • Date limite de soumission: 1er février 2020
  • Notification de la décision: 26 février 2020
  • Atelier  : 26 avril 2020

Joyeuses Fêtes!

Contribution de:
Kathleen Siminyu, Coordinatrice du réseau AI4D-Africa
Sackey Freshia, Jomo Kenyatta University of Agriculture and Technology
Daouda Tandiang Djiba, GalsenAI

AI4D – African Language Dataset Challenge // Défi AI4D – Jeu de Données sur les Langues Africaines

NLP Challenge

Getting started with programming is easy, a well-trodden path. Whether it be picking up the skill itself, a new programming language or venturing into a new domain, like Natural Language Processing (NLP), you can be sure that a variety of beginner tutorials exist to get you started. The ‘Hello World!’s, as you may know them. 

Where NLP is concerned, some paths tend to be better trodden than others. It is infinitely easier to accomplish an NLP task, say Sentiment Analysis, in English than it is to do the same in my mother tongue, Luhya. This reality is an extrapolation of the fact that the languages of the digital economy are major European languages.

The gap between languages with plenty of data available on the Internet and those without is ever increasing. Pre-trained language models in recent times have led to significant improvement in various NLP tasks and Transfer Learning is rapidly changing the field. While leading architectures for pre-training models for Transfer Learning in NLP are freely available for use, most are data-hungry. The GPT-2 model, for instance, used millions, possibly billions of text to train. (ref)

The only way I know how to begin closing this gap is by creating, uncovering and collating datasets for low resource languages. With the AI4D – African Language Dataset Challenge, we want to spur on some groundwork. While Deep Learning techniques now make it possible to dream of a future where NLP researchers and practitioners on the continent can easily innovate in the languages their communities speak, a future where literacy and mastery of a major European language is no longer a prerequisite to participation in the digital economy, these techniques require data. Data that can only be created by the communities that speak these languages, by individuals that have the technical skills, by those of us who understand the importance of this work and have the desire to undertake it.

The challenge will run for 5 months(November 2019 to March 2020), with cash prizes of USD 500 awarded as an incentive to the top 2 submissions each month. This is the first of a two-phase challenge. In this first phase, the creation of datasets. We would like to see some of these datasets developed for specific downstream tasks but this is not necessary. 

We have however earmarked four downstream NLP tasks and anticipate that one(or two) of these will be the framing of the second phase of this challenge; Sentence Classification, Sentiment Analysis, Question Answering and Machine Translation. Other downstream tasks that participants may be interested in developing datasets for, or have already developed datasets for, are also eligible. Our intention is that the datasets are kept free and open for public use under a Creative Commons license once the challenge is complete.

The challenge is hosted on Zindi, head on over to this page for full details, the prize money provided through a partnership between the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA), the facilitation of the challenge through combined efforts of the Artificial Intelligence for Development Network(AI4D-Africa) and the Knowledge 4 All Foundation(K4All), and finally, our expert panel that have volunteered their time to undertake the difficult qualitative aspect of dataset assessment; Jade Abbott – RetroRabbit, John Quinn – Google AI/Makerere University, Kathleen Siminyu – AI4D-Africa, Veselin Stoyanov – Facebook AI and Vukosi Marivate – University of Pretoria. 

The rest, we leave up to the community.  

Contribution by Kathleen Siminyu, AI4D-Africa Network Coordinator

Photo by Eva Blue on Unsplash.

Se lancer dans la programmation est facile, c’est un chemin bien balisé. Qu’il s’agisse de l’acquisition de la compétence elle-même, un nouveau langage de programmation ou vous aventurer dans un nouveau domaine, tel que le traitement du langage naturel (NLP), vous pouvez être sûr qu’il existe une variété de tutoriels pour débutants pour vous aider à démarrer. Les “Hello World!”, Comme vous les connaissez peut-être.


En ce qui concerne le traitement des langages (NLP) , certains chemins ont tendance  à être mieux balisés que d’autres. Par exemple en analyse sentimental, il est beaucoup plus facile d’accomplir une tâche de NLP  que de faire de même dans ma langue maternelle, Luhya. Cette réalité est une extrapolation du fait que les langues de l’économie numérique sont en majeur partie des  langues européennes.

L’écart entre les langues contenant beaucoup de données disponibles sur Internet et celles qui n’en possèdent pas ne cesse de se creuser. Les modèles linguistiques pré-entraînés  de ces dernières années ont conduit à une amélioration significative de diverses tâches du traitement des langages (NLP) et l’apprentissage par transfert (Transfer Learning) change rapidement le domaine. Bien que les principales architectures pour les modèles de pré-entraînés  à l’apprentissage par transfert en NLP soient librement utilisables, la plupart ont besoin de beaucoup de données. Le modèle GPT-2, par exemple, utilise des millions, voire des milliards de textes pour apprendre . (ref)

La seule façon pour moi de commencer à combler cet écart consiste à créer, à découvrir et à assembler des ensembles de données pour des langages disposant de peu de ressources. Avec le défi AI4D – Jeu de données sur les langues africaines, nous souhaitons stimuler le travail préparatoire. Bien que les techniques d’apprentissage en profondeur permettent désormais de rêver d’un avenir où les chercheurs et les praticiens en NLP  du continent pourront facilement innover dans les langues parlées par leurs communautés, un avenir où l’alphabétisation et la maîtrise d’une grande langue européenne n’est plus une condition préalable à la participation à la l’économie numérique, ces techniques nécessitent des données. Des données qui ne peuvent être créées que par les communautés qui parlent ces langues, par des personnes possédant les compétences techniques, par ceux d’entre nous qui comprenons l’importance de ce travail et qui souhaitent le faire.

Le défi durera 5 mois (de novembre 2019 à mars 2020), avec des prix en espèces de 500 USD attribués sous forme d’encouragement aux 2 meilleurs projets chaque mois. C’est le premier d’un défi en deux phases. Dans cette première phase, la création de jeux de données. Nous aimerions voir certains de ces jeux de données développés pour des tâches spécifiques en aval, mais ce n’est pas nécessaire.

Nous avons toutefois réservé quatre tâches du NLP  en aval et prévoyons qu’une (ou deux) d’entre elles constitueront le cadre de référence de la deuxième phase de ce défi. Classification de textes , analyse des sentiments, réponses aux questions et traduction automatique. Les autres tâches en aval pour lesquelles les participants pourraient  être intéressés par le développement de jeux de données ou pour lesquels ils ont déjà développé des jeux de données sont également éligibles. Notre intention est que les jeux de données restent libres et ouverts au public sous une licence “Creative Commons” une fois le challenge terminé.

Le défi est hébergé sur Zindi, rendez-vous sur cette page pour obtenir tous les détails, l’argent du prix fourni grâce au partenariat entre le Centre de recherches pour le développement international (CRDI) et l’Agence suédoise de coopération pour le développement international (SIDA), la facilitation du défi par les efforts combinés du réseau de l’intelligence artificielle pour le développement (AI4D-Africa) et de la fondation Knowledge 4 All (K4All), et enfin de notre groupe d’experts qui ont offert de leur temps pour aborder le difficile aspect qualitatif de l’évaluation d’un jeu  de données; Jade Abbott – RetroRabbit, John Quinn – Google AI / Université Makerere, Kathleen Siminyu – AI4D-Africa, Veselin Stoyanov – Facebook AI et Vukosi Marivate – Université de Pretoria.

Le reste, nous laissons à la communauté.

Contribution de Kathleen Siminyu, Coordinatrice du réseau AI4D-Africa

Photo par Eva Blue sur Unsplash.


UNESCO’s Dorothy Gordon: The Impact of Artificial Intelligence on International Development

IDRC’s President Jean Lebel invited Dorothy Gordon, the Chair of Information for All at UNESCO, to Montréal, where she shared her reflections on the potential impacts of artificial intelligence on international development. Here is a summary of her talk in Montréal in May 2019:

Dorothy’s talk focused on the opportunities of AI in the Global South and emphasized the continued need for international collaboration to foster practices that promote the responsible use of AI systems to benefit people. Looking at how technological innovations were deployed in many developing countries she also cautions that “a major problem with new technologies is that we have decision-makers with no ability to judge whether the technology they’re being sold is going to be able to deliver or not.”


Featured image by CORIM/Flickr 2019. Under Fair Use and no copyright infringement intended.

AI4D-Africa Innovation Call for Proposals – July 18th, 2019

Submit Your Proposal Today

This Call for Proposals invites individuals, grassroots organizations, initiatives, academic, and civil society institutions to submit their proposals for mini-projects within the 2019 Artificial Intelligence for Development (AI4D) initiative. In a recent AI4D workshop in Nairobi, the African community outlined a roadmap, including recommendations for capacity building, governance, and innovation for ethical and locally relevant AI research and a commitment to advance AI innovations that will be in aid of the sustainable development agenda of Africa.

The AI4D Africa call is anticipated to last for 6 weeks, from June 6th to July 18th. The projects selected for funding will be notified by August 16th, 2019 and will be invited to present their solutions at the AI4D workshop during the Deep Learning Indaba 2019 conference. Selected projects will be monetarily awarded from USD 5,000 to USD 8,000, funding which will be disbursed as the project progresses, based on agreed timelines and project deliverables.

Important dates:

  • June 6th, 2019 – Call for proposals release
  • July 18th, 2019 – Proposal submission deadline
  • August 1st, 2019 – Decision notification
  • September 1st, 2019 – Expected start of the projects
  • January 31st, 2020 – Reporting of the project outcomes

Proposals that fall within the below categories are highly encouraged as they fall within our focus areas, however, all proposals will be evaluated equally.

  • Future of Work
    • Microwork, new forms of employment, etc
  • Inclusion and gender
    • Text and speech datasets, tools and resources for African languages
    • Countering gender-based violence/harassment
    • Applications that benefit women’s empowerment and well being
    • Enabling the differently abled – tools and resources for the blind, deaf, etc.
  • Governance
    • Open Data
    • Government Service Delivery (procurement, social services, etc.)
  • Agriculture
    • Computer Vision for Plant and Animal Disease Diagnostics
  • Healthcare
    • Healthcare access
    • Computer Vision for Medical Diagnostics
    • Supply Chain Management
    • Natural language processing and disease outbreak surveillance
  • Education
    • Administrative Tasks Automation
    • Personalized Learning
    • Improving Learning Outcomes
  • Transportation
  • Disaster Management
  • Climate change

A strong proposal is one that meets any or all of the following requirements:

  • Result in the creation of a dataset. We propose that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses, and so on. See this paper for further details. In addition, we would like that the dataset is kept free and open for public use (with ethical provisions being taken into account).
  • Novel and motivated goal. Of particular interest are tasks that imply machine learning solutions different from the traditional setting, this includes legal, ethical and social aspects to the ML solutions.
  • Challenging yet manageable task with scalable long-term vision. The task should be challenging in the sense that there is enough room for iteration and improvement from the initial solution. An initial solution should be attainable in a timeframe of 3 to 4 months.
  • Domain accessibility to the general public. The notions presented in the project description should be accessible to the majority of machine learning and data mining practitioners, including ethicists, policymakers and other stakeholders from across sub-Saharan Africa who might not have excessive domain knowledge in AI or access to a powerful computational infrastructure.

Please keep the proposal concise, no more than 4 pages, including references.

Applications should include:

  • Full names of lead(s)
  • Affiliation(s)(if any)
  • Email address(es)
  • Phone number(s)
  • Clearly stated objectives and expected outcomes
  • Activity timelines
  • Budget allocation
  • A brief paragraph on long-term vision, if any
  • Any other information you think we should know

The submission deadline is Thursday, July 18th, 2019, at 0000hrs (anywhere on earth).

Please use this form to make your submission.

Watch the Live #AI4D Knowledge Webinar: Introducing the 2019 Government AI Readiness Index

Technologies have had an undeniable impact on improving living standards, connecting us together, and increasing productivity. With the right policies and institutional arrangements, governments can ensure that the benefits of artificial intelligence are shared broadly. This one-hour webinar will introduce and ignite a dialogue on this year’s Government AI Readiness Index and its implications on various regions. The dialogue will draw on the knowledge of leading regional experts who penned this report.

The Panelists

  • Hannah Miller, Report Lead Author from Oxford Insights, UK
  • Isaac Rutenberg, Director of the Centre for Intellectual Property and Information Technology Law (CIPIT), Kenya
  • Fabrizio Scrollini, Executive Director of the Latin American Open Data Initiative, Uruguay
  • Alex Comninos, AI4D Policy & Regulation Focal Point, Research ICT Africa, South Africa

This webinar was moderated by Robert Muthuri, AI4D Policy & Regulation Focal Point, Centre for Intellectual Property and Information Technology, Kenya

If you wish to rewatch or missed the sold-out free webinar session, see below clip (Listen to it as a podcast):


  • Wednesday, May 15, 2019, from 16h-17h PM Nairobi Time | 14h-15h PM London |  9h-10h AM ET – Ottawa Time 
  • This webinar will be hosted on Webex

The 2019 Government AI Readiness Index was produced by Oxford Insights with support from the International Development Research Centre (IDRC).

The Government AI Readiness Index 2019: More Equal Implementation Needed to Close Global Inequalities

In an era where AI-enabled systems already power a broad set of activities such as social media algorithms, autonomous vehicles, flight navigation systems, agricultural sensors, disease diagnosis, it is only poised to become ever-more pervasive. However, “AI can either accelerate the achievement of the [Sustainable Development] goals or entrench existing gaps,” says IDRC President Jean Lebel in a recent article.

With the right policies and institutional arrangements, governments can ensure that the benefits of disruptive technologies like artificial intelligence are shared broadly and help transform the way that public services are delivered and support better governance. The 2019 Government AI Readiness Index provides the first global panorama covering 194 countries and territories on governments’ preparedness to take advantage of the benefits of AI in their operations and delivery of public services.  

The 2019 ranking weighs several indicators for measuring government AI readiness with a focus on the responsible and ethical use of AI, weighting criteria such as the state of data protection and privacy laws and whether or not a national AI-related vision exists. The Index considers the availability of open government data, digital skills and education, and private sector capability to the scope and quality of digital public services, among others.

This year’s Index now includes in-depth analysis of AI readiness in regional contexts from regional experts. Regions and countries with strong economies, good governance, and innovative private sectors ranked higher on the Index. Governments in the Global North are still better positioned to reap the benefits of AI than their Southern counterparts, but there is an opportunity to address the imbalance. Otherwise, the differences in AI readiness between governments may increase the risk that certain countries could become testing grounds, where AI systems could be misused, and weaken the opportunities to use AI in support of the delivery for the common good and public welfare.

High-income countries with robust AI strategies and investments dominated the Top 10 spots of the Index. Singapore comes first, followed by the United Kingdom. Germany is third. Nordic countries – Finland, Sweden, and Denmark – secured top spots. Canada came in seventh place, ahead of France. Although Canada was the first country to announce a national AI strategy in early 2017 with a five-year $125-million plan, the index highlights opportunities for Canada to further invest in other dimensions of AI readiness.

Vibrant Artificial Intelligence Ecosystems in the Global South  

In contrast, Africa is the region positioned to benefit the most from improving and investing in AI readiness. No African countries came in the Top 50 with Kenya being edge out at 52nd. AI readiness in governments may have differing impacts in Africa than countries in the Top 10 such as Singapore. While the risk of AI-related automation of existing jobs in Africa is lower – for now – due to a larger informal sector, nevertheless AI may present new challenges and opportunities for fostering development in Africa. Governments in Africa will need to be ready to foster and encourage AI innovation through public and private sectors, to support new regulatory structures and deepen existing ones that will be needed to minimize potential harms, such as in health systems, and to consider how they will use AI-enabled technologies in their own policy making and service delivery.  

While Kenya is the only country out of the 46 sub-Saharan African states that has an AI-specific taskforce that is working towards a national strategy, the outlook for AI in Africa is positive. South Africa and Uganda recently announced the creation of their National Taskforces on emerging technologies. The development of AI is advancing at a rapid pace in sub-Saharan Africa. There is an important opportunity to support Government readiness in AI in order to ensure that countries from Africa are positioned to benefit from the potential of AI in their economies, health systems, service delivery and more. “African governments can capitalize on the late-mover advantage” to create smarter AI-related policies and institutional arrangements says Isaac Rutenberg, Director of the Centre for Intellectual Property and Information Technology Law (CIPIT) and a contributor to the Index report.

Latin America’s AI future is still uncertain due to structural inequalities and governance challenges like weak privacy laws needed to enable the formulation of a clear AI policy and ethical framework for the region. Mexico leads this region’s rankings followed by Uruguay, Chile, and Brazil. Local AI capacity needs to be further strengthened and work closely with the public sector as there is an important disconnect between the AI ​community and the public sector. Across this region, we need “more investment tailored for the Latin American context and the right ethical and policy framework to kickstart an inclusive AI development cycle,” says Fabrizio Scrollini, Executive Director of the Latin American Open Data Initiative (ILDA).

Over the last two years, the interest in informal developer communities, the private sector, and researchers point to the growth of a vibrant and diverse AI ecosystem in the Global South. Similarly, as the Index uses secondary data, relying on traditional metrics of counting the number of patent applications and scientific publications on machine learning may underestimate the level and activity of innovation across middle and low-income countries.

The AI Network of Excellence in sub-Saharan Africa

To reduce the gap in AI readiness, IDRC is developing proposals with the Latin American and African AI communities. In recent regional workshops in Nairobi and Mexico City,  the start of is a vibrant AI ecosystem was evident, with a number of emerging initiatives already across the Global South aiming to strengthen machine learning and artificial intelligence research and talent. A third regional workshop is planned for Asia in late 2019.  

For example, in Nairobi, sixty African and international experts at the workshop collaboratively shaped a new ambitious and pragmatic roadmap for the AI Network of Excellence in sub-Saharan Africa. The roadmap sets out a vision and the start of a new AI research and innovation agenda for development on how African countries can address existing imbalances and strengthen AI readiness – in government and policy spheres, in industry and applications, and in building emerging talent and skills.

What would happen if there are more than 30 African countries developing their own AI strategies in the next five years? Imagine if more than 400 PhDs in AI and machine learning from across Africa were innovating and engaging in shaping global conversations about how AI can be used to support human development. And what if universities, the private sector, and other public interest institutions invest a billion dollars in collaborations on AI to support the achievement of African Union’s development blueprint Agenda 2063? This would certainly be important steps to change the unequal picture presented in this year’s index.

AI’s profound potential for accelerating progress on the Sustainable Development Goals can only happen if it is well managed by governments and like-minded actors. This year’s  Government AI Readiness Index highlights the need for more equal implementation to close AI’s potential to widen global inequalities. With committed local AI community actors and global partners like Sweden, IDRC is looking to support collaborative approaches with researchers, enterprises, start-ups, policymakers, and civil societies to close this gap and make Canada a global partner in advancing effective and responsible development of AI for a more inclusive and sustainable world.  

The Index and report are licensed under a CC-BY. Follow us on Twitter @ai4dev.

To download the Government AI Readiness Index 2019 Report, click here. 





The commentary article was written by Kai-Hsin Hung, Katie Clancy, and Fernando Perini. This commentary article is the extensive version of the original Perspectives article published on IDRC’s website.

The Government AI Readiness Index 2019 is produced by Oxford Insights with support from the International Development Research Centre (IDRC). We would like to thank all the regional experts who contributed to this report.

Launch of the 2019 Government AI Readiness Index

2019 Government AI Readiness Index

Over the past few months, AI4D partner Oxford Insights, an international public sector consultancy specialising in AI, has been working with the IDRC to prepare the second edition of the Government AI Readiness Index. The Index measures governments’ readiness for implementing AI in their internal operations and in public service delivery. The 2019 edition of the Index will help inform the design of the IDRC’s Artificial Intelligence for Development initiative (AI4D). AI4D will focus on research and capacity-building aimed at developing AI applications that are inclusive, ethical, and rights-based.

The 2019 Government AI Readiness Index builds on the methodology developed in the first edition, which combined nine input metrics to produce a composite score for OECD governments. This year, the methodology has evolved to include 11 metrics, grouped under four high-level clusters: governance; infrastructure and data; skills and education; and government and public services. The data is derived from a variety of resources. These include desk research into AI strategies, databases such as the number of registered AI startups on Crunchbase, and indices such as the UN eGovernment Development Index.

We wanted this year’s index to be more globally representative than last year’s group of OECD governments, so we have expanded the scope to cover all UN countries. Having calculated scores for all 194 governments included in the report, we invited experts from each region to contribute commentary to help bring our findings to life with their insights and local knowledge.

It is hoped that the Index will serve to highlight any global disparities in terms of access to AI, to help prevent existing inequalities becoming further entrenched in the age of automation. Rather than fuelling a global ‘race for AI’, the Index is intended to aid policymakers around the world to identify areas where they are performing well, and areas which may require further attention. The inclusion of commentary by regional experts is intended to assist with this, by helping to contextualise governments’ AI readiness in a way that is not possible through quantitative research alone.

The 2019 Government AI Readiness Index scores and report have now been finalised, and are due to be published on Tuesday, 21st May.

Naser Faruqui from IDRC discussing AI and its impact for development

Play video by Naser Faruqui, IDRC, at workshop “Toward a Network of Excellence in Artificial Intelligence for Development (AI4D) in sub-Saharan Africa”, Nairobi, Kenya, April 2019

Naser in his work supports scientists and their innovations in developing countries to help solve their own problems. The confluence of bigdata, powerful computing and machine learning created a tipping point in powerful applications, and this can be transferred to development opportunities in the area of health, education and agriculture.

My bluesky is to ensure that Africans can fully contribute, participate and benefit from potential opportunities in AI