AI Governance Shared Value Incubator Blueprint Public Sector Policy Advisory Guide Business Model Design Sprint Guide ‘Made in Africa’ Evaluation (MAE) Toolkit Africanized Leadership Program Planner AI Governance Certification Platform GrmCoDesign Impact Funder Trust Audit Partnership Readiness Partnership Vetting Shared Value Incubator Blueprint Public Sector Policy Advisory Guide Business Model Design Sprint Guide ‘Made in Africa’ Evaluation (MAE) Toolkit Africanized Leadership Program Planner AI Governance Certification Platform GrmCoDesign Impact Funder Trust Audit Partnership Readiness Partnership Vetting Please enable JavaScript in your browser to complete this form. - Step 1 of 5NameAI Governance & Data Sovereignty Framework A comprehensive toolset to audit AI systems for ethical risks and bias, ensuring alignment with African-centric data sovereignty and accountability standards.Purpose & GoalsPurpose & GoalsNextPillar 1: Community & Collective BenefitObjectiveAudit QuestionsCommunity Impact Assessment: How has the community been involved in defining the problem and the potential benefits/harms of the AI system?Collective Benefit: Does the AI system prioritize collective well-being over individual or corporate profit? How is value shared?Local Context Adaptation: How is the AI system designed to be adaptable to diverse local contexts, languages, and cultural norms?PreviousNextPillar 2: Data Sovereignty & ReciprocityObjectiveAudit QuestionsConsent & Ownership: How is informed consent obtained, and who owns the data and the resulting AI models?Data Reciprocity: What tangible value or benefit does the community receive in exchange for their data?Data Governance: What community-led mechanisms are in place for data governance and oversight?PreviousNextPillar 3: Algorithmic Fairness & Transparency corporate Assessment: the ObjectiveAudit QuestionsBias Audit: How have you audited the training data and model for biases relevant to the African context (e.g., ethnic, linguistic, gender)?Explainability: How are the AI system's decisions made transparent and understandable to the affected communities?Human-in-the-Loop & Grievance: What mechanisms are in place for human oversight and for communities to contest or seek redress from algorithmic decisions?PreviousNextAPA Toolset for ImplementationData Sovereignty Agreements (Template)AI Bias Audit FrameworkGRM for AI Systems (Co-design Guide)Community-Led Impact Assessment for AIGenerate AI-Powered Report
