Research Papers

The Deep Learning Indaba (DLI) 2026 advances its commitment to elevating leading AI research from Africa by expanding its research track to include full-length, indexed papers. This initiative builds on a strong foundation of poster sessions and research papers, and aims to enhance the global visibility and academic recognition of high-quality African AI scholarship.

For DLI 2026, paper submission is now an independent pathway to conference attendance; authors may submit papers regardless of their application or acceptance status for the main event. One author per accepted paper will receive a complimentary attendance pass, with priority given to African researchers or those based in Africa.

We invite submissions of original, high-impact research in Machine Learning, Natural Language Processing, Deep Learning, and AI, particularly work addressing Africa’s distinct challenges and opportunities. Accepted papers will be presented at the Research in Africa Showcase to foster engagement with leading researchers, industry practitioners, and policymakers. 

Peer-reviewed accepted papers will be published in a special volume of the International Joint Conference on Artificial Intelligence (IJCAI).

Scope

For DLI 2026, we emphasise AI research and applications grounded in the African context, addressing the continent’s unique challenges, priorities, and opportunities. We invite submissions ranging from foundational AI research to impactful real-world applications that advance innovation, inclusive economic growth, and sustainable development across key sectors, including healthcare, education, agriculture, and finance. Topics of interest include, but are not limited to:

• Machine Learning and Deep Learning: Design, analysis, and optimisation of learning algorithms, including novel model architectures, efficient training techniques, scalable learning systems, and deployment of ML/DL solutions in resource-constrained environments common across many regions in Africa.
• Natural Language Processing (NLP): Research on language technologies, including language models, speech and text processing, multilingual and cross-lingual NLP, with special emphasis on low-resource, indigenous, and underrepresented African languages, as well as on language technologies for inclusion, education, and governance.
• Computer Vision and Multimedia Analysis: Image and video understanding, medical imaging, biometric systems, remote sensing, satellite imagery, and geospatial analysis, including applications in public health, agriculture, urban planning, climate monitoring, and environmental conservation across Africa.
• Reinforcement Learning and Multi-Agent Systems: Learning-based decision-making, planning, control, and coordination in dynamic and uncertain environments, including autonomous systems, smart infrastructure, robotics, and simulations relevant to transportation, energy systems, and smart cities in emerging economies
• Datasets and Benchmarks The Datasets and Benchmarks Track is dedicated to fostering the development, sharing, and evaluation of datasets and benchmarks that are valuable for the African ecosystem community. We seek contributions that introduce novel datasets, propose new benchmarks, or offer tools and methodologies for dataset creation, curation, and evaluation.
• AI for Social Impact and Sustainable Systems: Algorithmic, data-driven, and systems-level AI approaches for addressing large-scale societal and developmental challenges, including intelligent decision-support systems for healthcare delivery, learning analytics and adaptive education platforms, financial technologies for inclusion and risk modelling, precision agriculture and food systems optimisation, climate modelling and environmental monitoring, disaster prediction and response, and AI-enabled public service delivery. Contributions may address scalability, robustness, deployment in low-resource settings, and alignment with regional development goals and the UN Sustainable Development Goals.
• Privacy-Preserving, Ethical, and Trustworthy Artificial Intelligence: Theoretical and applied research on secure, privacy-aware, and trustworthy AI systems, including federated and distributed learning, differential privacy, secure multi-party computation, and privacy-preserving inference. Topics also include algorithmic fairness and bias mitigation, accountability and governance mechanisms, transparency and explainability (XAI), robustness to adversarial and data poisoning attacks, and the principled design of AI systems that comply with legal, ethical, and socio-cultural constraints across diverse populations.