This document was prepared by the Centre on Knowledge Governance (CKG) to summarise discussions on principles of Just AI and Knowledge Governance held in the Cradle of Humankind, South Africa, in February 2026. The Cradle discussions included representatives of African governments, academics, Artificial Intelligence (AI) model developers, scholars and negotiators of international agreements on traditional knowledge, and creators and performers of copyrighted works and data used in AI training, listed at the bottom of the document. The objective of the discussions was to articulate an African voice and focus on the topic while reflecting principles with potentially global application. This document records the substance of discussions without attempting to resolve any potential conflicts in views or endorsing any particular norms as reflecting the positions of all or any of the participants. When we use the word “should” we mean it to indicate that at least some members of the discussions proposed that actions should be taken, not necessarily that all in the room consented to the precise framing used here. It is not a suggestion for a legal document. It is the starting point, not an end, of a longer and broader discussion that CKG plans to host, including in consultation with other stakeholders, academics and policy makers. Table of contents Just AI Essence and purpose Principles Needs of African AI Developers From Knowledge Governance Systems From Other Resources From Exercise of Agency (code of conduct) Needs of Creators of Works and Content Used in Training From Knowledge Governance Systems From Other Resources From Exercise of Agency Objectives of Copyright and Knowledge Governance Objectives Table: Scale of Tools Objectives for Protecting Traditional Knowledge What Governments should do Workshop Participants Just AI Essence and purpose The goal of AI models and applications should be to serve as a tool to help humans, not to achieve an intelligence independent of humanity. AI should support human life and the planet. Models and tools should be decentralised and context specific, wherever possible; be as small (e.g. use as little data and compute power) as possible; be adaptable and malleable, be publicly accountable and transparent (whilst respecting privacy); reflect the lived experiences and cultures of the creators, developers and users, and accessible by all people wherever possible. Definitions of Public AI and Just AI Public AI models and tools are subject to public policies to ensure that they promote human rights and justice. Public AI policies aim to ensure that AI development is in the public interest, subject to transparent governance and democratic public accountability. Just AI builds on Public AI, aiming to protect the moral and material interests of creators, the stewardship of traditional knowledge, cultural expressions, and genetic resources by communities, and by promoting the developmental priorities of the Global South. Principles Just AI systems should not exploit labour, use unsustainable environmental practices, be concentrated in and controlled by large for-profit corporations in one or two countries. Just AI systems require recognition of data providers and data communities who are impacted, have guardrails to prevent abuse (such as deepfakes), and should prevent the misappropriation of traditional knowledge and traditional cultural expressions. African governments should explore strategies to support Public AI, where compute, data, models, and expertise are made available to innovators and stakeholders on the continent. Needs of African AI Developers From Knowledge Governance Systems African AI developers need knowledge governance systems, including balanced copyright and other intellectual property provisions, that promote the ability to fairly use and access data for the development of models and tools. For example, fair use of the JW300 dataset of language translations was required to create African language translation tools by the Masakhane Natural Language Processing (NLP) project for African languages. NLP also needs access to other sources that have local language translations, including content from broadcasters (especially public broadcasters). Content and data needed are often held by or created by governments, but not always released through open access policies. There is also a need for access to African data and information held abroad, such as in foreign libraries, museums, archives, and media collections. African AI developers need the ability to use reverse engineering and knowledge distillation to learn from foundational large language models to create smaller and more specific applications for the African context. Reverse engineering rights are recognised in many systems of the Global North, but often lack expression in the copyright and other laws of the Global South. African AI developers often wish to ensure protection of their own curated datasets against competing global for-profit institutions. A particular concern is expressed about the rapacious desire for more data by the largest model builders, who rarely share back with African producers of data or model builders. Positive approaches include the use of the Nwulite Obodo Open Data License (Noodl), that includes customised license terms and tiered pricing schemes for the distribution of African language datasets. From Other Resources African AI developers need access to a variety of resources to do their work. They need affordable access to a stable Internet and computing infrastructure. Computing power can be accessed through a hybrid of public and private infrastructure. Some examples include African GPU Hub, Sannah AI, MIND institute. There is a need for access to affordable legal advice and support, for example, from legal networks that understand the law and the needs of AI developers. Developers need access to various kinds of information and training, including access to performance metrics of public AI, information on data provenance, information on IP and data protection laws, and participation in the development of public policies and strategies. From Exercise of Agency (code of conduct) Developers need to establish norms for their own conduct, similar to the Malabo convention and South African NIMA. Organisation should be furthered through support for structures to engage each other and have a strong voice with policy makers (such as through the Deep Learning Indaba). In their work, developers need to prioritise the African context and cultural sensitivity in their work, prioritise local over global
